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Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

donnemartin/system-design-primer

Folders and files.

The System Design Primer

Learn how to design large-scale systems. Prep for the system design interview.

Learn how to design large-scale systems

Learning how to design scalable systems will help you become a better engineer.

System design is a broad topic. There is a vast amount of resources scattered throughout the web on system design principles.

This repo is an organized collection of resources to help you learn how to build systems at scale.

Learn from the open source community

This is a continually updated, open source project.

Contributions are welcome!

Prep for the system design interview

In addition to coding interviews, system design is a required component of the technical interview process at many tech companies.

Practice common system design interview questions and compare your results with sample solutions : discussions, code, and diagrams.

Additional topics for interview prep:

Study guide

How to approach a system design interview question.

• System design interview questions, with solutions
• Object-oriented design interview questions, with solutions

Additional system design interview questions

Anki flashcards.

The provided Anki flashcard decks use spaced repetition to help you retain key system design concepts.

• System design deck
• System design exercises deck
• Object oriented design exercises deck

Great for use while on-the-go.

Coding Resource: Interactive Coding Challenges

Looking for resources to help you prep for the Coding Interview ?

Check out the sister repo Interactive Coding Challenges , which contains an additional Anki deck:

• Coding deck

Contributing

Learn from the community.

Feel free to submit pull requests to help:

• Improve sections
• Add new sections

Content that needs some polishing is placed under development .

Review the Contributing Guidelines .

Index of system design topics

Summaries of various system design topics, including pros and cons. Everything is a trade-off . Each section contains links to more in-depth resources.

Step 1: Review the scalability video lecture

Step 2: review the scalability article, performance vs scalability, latency vs throughput, cp - consistency and partition tolerance, ap - availability and partition tolerance, weak consistency, eventual consistency, strong consistency, replication, availability in numbers, domain name system, active-passive, active-active.

• Layer 4 load balancing
• Layer 7 load balancing

Horizontal scaling

Load balancer vs reverse proxy, microservices.

• Service discovery

Master-slave replication

Master-master replication.

• Denormalization

Key-value store

Document store, wide column store.

• Graph Database

SQL or NoSQL

Client caching, cdn caching, web server caching, database caching, application caching, caching at the database query level, caching at the object level, cache-aside, write-through, write-behind (write-back), refresh-ahead, message queues, task queues, back pressure, transmission control protocol (tcp), user datagram protocol (udp), remote procedure call (rpc), representational state transfer (rest), powers of two table, latency numbers every programmer should know, real world architectures, company architectures, company engineering blogs, under development, contact info.

Suggested topics to review based on your interview timeline (short, medium, long).

Q: For interviews, do I need to know everything here?

A: No, you don't need to know everything here to prepare for the interview .

What you are asked in an interview depends on variables such as:

• How much experience you have
• What your technical background is
• What positions you are interviewing for
• Which companies you are interviewing with

More experienced candidates are generally expected to know more about system design. Architects or team leads might be expected to know more than individual contributors. Top tech companies are likely to have one or more design interview rounds.

Start broad and go deeper in a few areas. It helps to know a little about various key system design topics. Adjust the following guide based on your timeline, experience, what positions you are interviewing for, and which companies you are interviewing with.

• Short timeline - Aim for breadth with system design topics. Practice by solving some interview questions.
• Medium timeline - Aim for breadth and some depth with system design topics. Practice by solving many interview questions.
• Long timeline - Aim for breadth and more depth with system design topics. Practice by solving most interview questions.

How to tackle a system design interview question.

The system design interview is an open-ended conversation . You are expected to lead it.

You can use the following steps to guide the discussion. To help solidify this process, work through the System design interview questions with solutions section using the following steps.

Step 1: Outline use cases, constraints, and assumptions

Gather requirements and scope the problem. Ask questions to clarify use cases and constraints. Discuss assumptions.

• Who is going to use it?
• How are they going to use it?
• How many users are there?
• What does the system do?
• What are the inputs and outputs of the system?
• How much data do we expect to handle?
• How many requests per second do we expect?
• What is the expected read to write ratio?

Step 2: Create a high level design

Outline a high level design with all important components.

• Sketch the main components and connections
• Justify your ideas

Step 3: Design core components

Dive into details for each core component. For example, if you were asked to design a url shortening service , discuss:

• MD5 and Base62
• Hash collisions
• Database schema
• Database lookup
• API and object-oriented design

Step 4: Scale the design

Identify and address bottlenecks, given the constraints. For example, do you need the following to address scalability issues?

Load balancer

• Database sharding

Discuss potential solutions and trade-offs. Everything is a trade-off. Address bottlenecks using principles of scalable system design .

Back-of-the-envelope calculations

You might be asked to do some estimates by hand. Refer to the Appendix for the following resources:

• Use back of the envelope calculations

Source(s) and further reading

Check out the following links to get a better idea of what to expect:

• How to ace a systems design interview
• The system design interview
• Intro to Architecture and Systems Design Interviews
• System design template

System design interview questions with solutions

Common system design interview questions with sample discussions, code, and diagrams. Solutions linked to content in the solutions/ folder.

Design Pastebin.com (or Bit.ly)

View exercise and solution

Design the Twitter timeline and search (or Facebook feed and search)

Design a web crawler

Design Mint.com

Design the data structures for a social network

Design a key-value store for a search engine

Design Amazon's sales ranking by category feature

Design a system that scales to millions of users on AWS

Object-oriented design interview questions with solutions

Common object-oriented design interview questions with sample discussions, code, and diagrams. Solutions linked to content in the solutions/ folder.

Note: This section is under development

System design topics: start here

New to system design?

First, you'll need a basic understanding of common principles, learning about what they are, how they are used, and their pros and cons.

Scalability Lecture at Harvard

• Vertical scaling
• Load balancing
• Database replication
• Database partitioning
• Scalability

Asynchronism

Next, we'll look at high-level trade-offs:

Availability vs consistency

Keep in mind that everything is a trade-off .

Then we'll dive into more specific topics such as DNS, CDNs, and load balancers.

A service is scalable if it results in increased performance in a manner proportional to resources added. Generally, increasing performance means serving more units of work, but it can also be to handle larger units of work, such as when datasets grow. 1

Another way to look at performance vs scalability:

• If you have a performance problem, your system is slow for a single user.
• If you have a scalability problem, your system is fast for a single user but slow under heavy load.
• A word on scalability
• Scalability, availability, stability, patterns

Latency is the time to perform some action or to produce some result.

Throughput is the number of such actions or results per unit of time.

Generally, you should aim for maximal throughput with acceptable latency .

• Understanding latency vs throughput

CAP theorem

In a distributed computer system, you can only support two of the following guarantees:

• Consistency - Every read receives the most recent write or an error
• Availability - Every request receives a response, without guarantee that it contains the most recent version of the information
• Partition Tolerance - The system continues to operate despite arbitrary partitioning due to network failures

Networks aren't reliable, so you'll need to support partition tolerance. You'll need to make a software tradeoff between consistency and availability.

Waiting for a response from the partitioned node might result in a timeout error. CP is a good choice if your business needs require atomic reads and writes.

Responses return the most readily available version of the data available on any node, which might not be the latest. Writes might take some time to propagate when the partition is resolved.

AP is a good choice if the business needs to allow for eventual consistency or when the system needs to continue working despite external errors.

• CAP theorem revisited
• A plain english introduction to CAP theorem
• The CAP theorem

Consistency patterns

With multiple copies of the same data, we are faced with options on how to synchronize them so clients have a consistent view of the data. Recall the definition of consistency from the CAP theorem - Every read receives the most recent write or an error.

After a write, reads may or may not see it. A best effort approach is taken.

This approach is seen in systems such as memcached. Weak consistency works well in real time use cases such as VoIP, video chat, and realtime multiplayer games. For example, if you are on a phone call and lose reception for a few seconds, when you regain connection you do not hear what was spoken during connection loss.

After a write, reads will eventually see it (typically within milliseconds). Data is replicated asynchronously.

This approach is seen in systems such as DNS and email. Eventual consistency works well in highly available systems.

After a write, reads will see it. Data is replicated synchronously.

This approach is seen in file systems and RDBMSes. Strong consistency works well in systems that need transactions.

• Transactions across data centers

Availability patterns

There are two complementary patterns to support high availability: fail-over and replication .

With active-passive fail-over, heartbeats are sent between the active and the passive server on standby. If the heartbeat is interrupted, the passive server takes over the active's IP address and resumes service.

The length of downtime is determined by whether the passive server is already running in 'hot' standby or whether it needs to start up from 'cold' standby. Only the active server handles traffic.

Active-passive failover can also be referred to as master-slave failover.

In active-active, both servers are managing traffic, spreading the load between them.

If the servers are public-facing, the DNS would need to know about the public IPs of both servers. If the servers are internal-facing, application logic would need to know about both servers.

Active-active failover can also be referred to as master-master failover.

Disadvantage(s): failover

• Fail-over adds more hardware and additional complexity.
• There is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive.

Master-slave and master-master

This topic is further discussed in the Database section:

Availability is often quantified by uptime (or downtime) as a percentage of time the service is available. Availability is generally measured in number of 9s--a service with 99.99% availability is described as having four 9s.

99.9% availability - three 9s

99.99% availability - four 9s

Availability in parallel vs in sequence

If a service consists of multiple components prone to failure, the service's overall availability depends on whether the components are in sequence or in parallel.

In sequence

Overall availability decreases when two components with availability < 100% are in sequence:

If both Foo and Bar each had 99.9% availability, their total availability in sequence would be 99.8%.

In parallel

Overall availability increases when two components with availability < 100% are in parallel:

If both Foo and Bar each had 99.9% availability, their total availability in parallel would be 99.9999%.

A Domain Name System (DNS) translates a domain name such as www.example.com to an IP address.

DNS is hierarchical, with a few authoritative servers at the top level. Your router or ISP provides information about which DNS server(s) to contact when doing a lookup. Lower level DNS servers cache mappings, which could become stale due to DNS propagation delays. DNS results can also be cached by your browser or OS for a certain period of time, determined by the time to live (TTL) .

• NS record (name server) - Specifies the DNS servers for your domain/subdomain.
• MX record (mail exchange) - Specifies the mail servers for accepting messages.
• A record (address) - Points a name to an IP address.
• CNAME (canonical) - Points a name to another name or CNAME (example.com to www.example.com ) or to an A record.

Services such as CloudFlare and Route 53 provide managed DNS services. Some DNS services can route traffic through various methods:

• Prevent traffic from going to servers under maintenance
• Balance between varying cluster sizes
• A/B testing
• Latency-based
• Geolocation-based

Disadvantage(s): DNS

• Accessing a DNS server introduces a slight delay, although mitigated by caching described above.
• DNS server management could be complex and is generally managed by governments, ISPs, and large companies .
• DNS services have recently come under DDoS attack , preventing users from accessing websites such as Twitter without knowing Twitter's IP address(es).
• DNS architecture
• DNS articles

Content delivery network

A content delivery network (CDN) is a globally distributed network of proxy servers, serving content from locations closer to the user. Generally, static files such as HTML/CSS/JS, photos, and videos are served from CDN, although some CDNs such as Amazon's CloudFront support dynamic content. The site's DNS resolution will tell clients which server to contact.

Serving content from CDNs can significantly improve performance in two ways:

• Users receive content from data centers close to them
• Your servers do not have to serve requests that the CDN fulfills

Push CDNs receive new content whenever changes occur on your server. You take full responsibility for providing content, uploading directly to the CDN and rewriting URLs to point to the CDN. You can configure when content expires and when it is updated. Content is uploaded only when it is new or changed, minimizing traffic, but maximizing storage.

Sites with a small amount of traffic or sites with content that isn't often updated work well with push CDNs. Content is placed on the CDNs once, instead of being re-pulled at regular intervals.

Pull CDNs grab new content from your server when the first user requests the content. You leave the content on your server and rewrite URLs to point to the CDN. This results in a slower request until the content is cached on the CDN.

A time-to-live (TTL) determines how long content is cached. Pull CDNs minimize storage space on the CDN, but can create redundant traffic if files expire and are pulled before they have actually changed.

Sites with heavy traffic work well with pull CDNs, as traffic is spread out more evenly with only recently-requested content remaining on the CDN.

Disadvantage(s): CDN

• CDN costs could be significant depending on traffic, although this should be weighed with additional costs you would incur not using a CDN.
• Content might be stale if it is updated before the TTL expires it.
• CDNs require changing URLs for static content to point to the CDN.
• Globally distributed content delivery
• The differences between push and pull CDNs

Load balancers distribute incoming client requests to computing resources such as application servers and databases. In each case, the load balancer returns the response from the computing resource to the appropriate client. Load balancers are effective at:

• Preventing requests from going to unhealthy servers
• Preventing overloading resources
• Helping to eliminate a single point of failure

Load balancers can be implemented with hardware (expensive) or with software such as HAProxy.

Additional benefits include:

• Removes the need to install X.509 certificates on each server
• Session persistence - Issue cookies and route a specific client's requests to same instance if the web apps do not keep track of sessions

To protect against failures, it's common to set up multiple load balancers, either in active-passive or active-active mode.

Load balancers can route traffic based on various metrics, including:

• Least loaded
• Session/cookies
• Round robin or weighted round robin

Layer 4 load balancers look at info at the transport layer to decide how to distribute requests. Generally, this involves the source, destination IP addresses, and ports in the header, but not the contents of the packet. Layer 4 load balancers forward network packets to and from the upstream server, performing Network Address Translation (NAT) .

Layer 7 load balancers look at the application layer to decide how to distribute requests. This can involve contents of the header, message, and cookies. Layer 7 load balancers terminate network traffic, reads the message, makes a load-balancing decision, then opens a connection to the selected server. For example, a layer 7 load balancer can direct video traffic to servers that host videos while directing more sensitive user billing traffic to security-hardened servers.

At the cost of flexibility, layer 4 load balancing requires less time and computing resources than Layer 7, although the performance impact can be minimal on modern commodity hardware.

Load balancers can also help with horizontal scaling, improving performance and availability. Scaling out using commodity machines is more cost efficient and results in higher availability than scaling up a single server on more expensive hardware, called Vertical Scaling . It is also easier to hire for talent working on commodity hardware than it is for specialized enterprise systems.

Disadvantage(s): horizontal scaling

• Servers should be stateless: they should not contain any user-related data like sessions or profile pictures
• Sessions can be stored in a centralized data store such as a database (SQL, NoSQL) or a persistent cache (Redis, Memcached)
• Downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out

Disadvantage(s): load balancer

• The load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly.
• Introducing a load balancer to help eliminate a single point of failure results in increased complexity.
• A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.
• NGINX architecture
• HAProxy architecture guide
• ELB listener config

Reverse proxy (web server)

A reverse proxy is a web server that centralizes internal services and provides unified interfaces to the public. Requests from clients are forwarded to a server that can fulfill it before the reverse proxy returns the server's response to the client.

• Increased security - Hide information about backend servers, blacklist IPs, limit number of connections per client
• Increased scalability and flexibility - Clients only see the reverse proxy's IP, allowing you to scale servers or change their configuration
• Compression - Compress server responses
• Caching - Return the response for cached requests
• HTML/CSS/JS
• Deploying a load balancer is useful when you have multiple servers. Often, load balancers route traffic to a set of servers serving the same function.
• Reverse proxies can be useful even with just one web server or application server, opening up the benefits described in the previous section.
• Solutions such as NGINX and HAProxy can support both layer 7 reverse proxying and load balancing.

Disadvantage(s): reverse proxy

• Introducing a reverse proxy results in increased complexity.
• A single reverse proxy is a single point of failure, configuring multiple reverse proxies (ie a failover ) further increases complexity.
• Reverse proxy vs load balancer

Application layer

Separating out the web layer from the application layer (also known as platform layer) allows you to scale and configure both layers independently. Adding a new API results in adding application servers without necessarily adding additional web servers. The single responsibility principle advocates for small and autonomous services that work together. Small teams with small services can plan more aggressively for rapid growth.

Workers in the application layer also help enable asynchronism .

Related to this discussion are microservices , which can be described as a suite of independently deployable, small, modular services. Each service runs a unique process and communicates through a well-defined, lightweight mechanism to serve a business goal. 1

Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc.

Service Discovery

Systems such as Consul , Etcd , and Zookeeper can help services find each other by keeping track of registered names, addresses, and ports. Health checks help verify service integrity and are often done using an HTTP endpoint. Both Consul and Etcd have a built in key-value store that can be useful for storing config values and other shared data.

Disadvantage(s): application layer

• Adding an application layer with loosely coupled services requires a different approach from an architectural, operations, and process viewpoint (vs a monolithic system).
• Microservices can add complexity in terms of deployments and operations.
• Intro to architecting systems for scale
• Crack the system design interview
• Service oriented architecture
• Introduction to Zookeeper
• Here's what you need to know about building microservices

Relational database management system (RDBMS)

A relational database like SQL is a collection of data items organized in tables.

ACID is a set of properties of relational database transactions .

• Atomicity - Each transaction is all or nothing
• Consistency - Any transaction will bring the database from one valid state to another
• Isolation - Executing transactions concurrently has the same results as if the transactions were executed serially
• Durability - Once a transaction has been committed, it will remain so

There are many techniques to scale a relational database: master-slave replication , master-master replication , federation , sharding , denormalization , and SQL tuning .

The master serves reads and writes, replicating writes to one or more slaves, which serve only reads. Slaves can also replicate to additional slaves in a tree-like fashion. If the master goes offline, the system can continue to operate in read-only mode until a slave is promoted to a master or a new master is provisioned.

Disadvantage(s): master-slave replication

• Additional logic is needed to promote a slave to a master.
• See Disadvantage(s): replication for points related to both master-slave and master-master.

Both masters serve reads and writes and coordinate with each other on writes. If either master goes down, the system can continue to operate with both reads and writes.

Disadvantage(s): master-master replication

• You'll need a load balancer or you'll need to make changes to your application logic to determine where to write.
• Most master-master systems are either loosely consistent (violating ACID) or have increased write latency due to synchronization.
• Conflict resolution comes more into play as more write nodes are added and as latency increases.

Disadvantage(s): replication

• There is a potential for loss of data if the master fails before any newly written data can be replicated to other nodes.
• Writes are replayed to the read replicas. If there are a lot of writes, the read replicas can get bogged down with replaying writes and can't do as many reads.
• The more read slaves, the more you have to replicate, which leads to greater replication lag.
• On some systems, writing to the master can spawn multiple threads to write in parallel, whereas read replicas only support writing sequentially with a single thread.
• Replication adds more hardware and additional complexity.

Source(s) and further reading: replication

• Multi-master replication

Federation (or functional partitioning) splits up databases by function. For example, instead of a single, monolithic database, you could have three databases: forums , users , and products , resulting in less read and write traffic to each database and therefore less replication lag. Smaller databases result in more data that can fit in memory, which in turn results in more cache hits due to improved cache locality. With no single central master serializing writes you can write in parallel, increasing throughput.

Disadvantage(s): federation

• Federation is not effective if your schema requires huge functions or tables.
• You'll need to update your application logic to determine which database to read and write.
• Joining data from two databases is more complex with a server link .
• Federation adds more hardware and additional complexity.

Source(s) and further reading: federation

• Scaling up to your first 10 million users

Sharding distributes data across different databases such that each database can only manage a subset of the data. Taking a users database as an example, as the number of users increases, more shards are added to the cluster.

Similar to the advantages of federation , sharding results in less read and write traffic, less replication, and more cache hits. Index size is also reduced, which generally improves performance with faster queries. If one shard goes down, the other shards are still operational, although you'll want to add some form of replication to avoid data loss. Like federation, there is no single central master serializing writes, allowing you to write in parallel with increased throughput.

Common ways to shard a table of users is either through the user's last name initial or the user's geographic location.

Disadvantage(s): sharding

• You'll need to update your application logic to work with shards, which could result in complex SQL queries.
• Rebalancing adds additional complexity. A sharding function based on consistent hashing can reduce the amount of transferred data.
• Joining data from multiple shards is more complex.
• Sharding adds more hardware and additional complexity.

Source(s) and further reading: sharding

• The coming of the shard
• Shard database architecture
• Consistent hashing

Denormalization attempts to improve read performance at the expense of some write performance. Redundant copies of the data are written in multiple tables to avoid expensive joins. Some RDBMS such as PostgreSQL and Oracle support materialized views which handle the work of storing redundant information and keeping redundant copies consistent.

Once data becomes distributed with techniques such as federation and sharding , managing joins across data centers further increases complexity. Denormalization might circumvent the need for such complex joins.

In most systems, reads can heavily outnumber writes 100:1 or even 1000:1. A read resulting in a complex database join can be very expensive, spending a significant amount of time on disk operations.

Disadvantage(s): denormalization

• Data is duplicated.
• Constraints can help redundant copies of information stay in sync, which increases complexity of the database design.
• A denormalized database under heavy write load might perform worse than its normalized counterpart.

Source(s) and further reading: denormalization

SQL tuning is a broad topic and many books have been written as reference.

It's important to benchmark and profile to simulate and uncover bottlenecks.

• Benchmark - Simulate high-load situations with tools such as ab .
• Profile - Enable tools such as the slow query log to help track performance issues.

Benchmarking and profiling might point you to the following optimizations.

Tighten up the schema

• MySQL dumps to disk in contiguous blocks for fast access.
• CHAR effectively allows for fast, random access, whereas with VARCHAR , you must find the end of a string before moving onto the next one.
• Use TEXT for large blocks of text such as blog posts. TEXT also allows for boolean searches. Using a TEXT field results in storing a pointer on disk that is used to locate the text block.
• Use INT for larger numbers up to 2^32 or 4 billion.
• Use DECIMAL for currency to avoid floating point representation errors.
• Avoid storing large BLOBS , store the location of where to get the object instead.
• VARCHAR(255) is the largest number of characters that can be counted in an 8 bit number, often maximizing the use of a byte in some RDBMS.
• Set the NOT NULL constraint where applicable to improve search performance .

Use good indices

• Columns that you are querying ( SELECT , GROUP BY , ORDER BY , JOIN ) could be faster with indices.
• Indices are usually represented as self-balancing B-tree that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time.
• Placing an index can keep the data in memory, requiring more space.
• Writes could also be slower since the index also needs to be updated.
• When loading large amounts of data, it might be faster to disable indices, load the data, then rebuild the indices.

Avoid expensive joins

• Denormalize where performance demands it.

Partition tables

• Break up a table by putting hot spots in a separate table to help keep it in memory.

Tune the query cache

• In some cases, the query cache could lead to performance issues .

Source(s) and further reading: SQL tuning

• Tips for optimizing MySQL queries
• Is there a good reason i see VARCHAR(255) used so often?
• How do null values affect performance?
• Slow query log

NoSQL is a collection of data items represented in a key-value store , document store , wide column store , or a graph database . Data is denormalized, and joins are generally done in the application code. Most NoSQL stores lack true ACID transactions and favor eventual consistency .

BASE is often used to describe the properties of NoSQL databases. In comparison with the CAP Theorem , BASE chooses availability over consistency.

• Basically available - the system guarantees availability.
• Soft state - the state of the system may change over time, even without input.
• Eventual consistency - the system will become consistent over a period of time, given that the system doesn't receive input during that period.

In addition to choosing between SQL or NoSQL , it is helpful to understand which type of NoSQL database best fits your use case(s). We'll review key-value stores , document stores , wide column stores , and graph databases in the next section.

Abstraction: hash table

A key-value store generally allows for O(1) reads and writes and is often backed by memory or SSD. Data stores can maintain keys in lexicographic order , allowing efficient retrieval of key ranges. Key-value stores can allow for storing of metadata with a value.

Key-value stores provide high performance and are often used for simple data models or for rapidly-changing data, such as an in-memory cache layer. Since they offer only a limited set of operations, complexity is shifted to the application layer if additional operations are needed.

A key-value store is the basis for more complex systems such as a document store, and in some cases, a graph database.

Source(s) and further reading: key-value store

• Key-value database
• Disadvantages of key-value stores
• Redis architecture
• Memcached architecture

Abstraction: key-value store with documents stored as values

A document store is centered around documents (XML, JSON, binary, etc), where a document stores all information for a given object. Document stores provide APIs or a query language to query based on the internal structure of the document itself. Note, many key-value stores include features for working with a value's metadata, blurring the lines between these two storage types.

Based on the underlying implementation, documents are organized by collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other.

Some document stores like MongoDB and CouchDB also provide a SQL-like language to perform complex queries. DynamoDB supports both key-values and documents.

Document stores provide high flexibility and are often used for working with occasionally changing data.

Source(s) and further reading: document store

• Document-oriented database
• MongoDB architecture
• CouchDB architecture
• Elasticsearch architecture

Abstraction: nested map ColumnFamily<RowKey, Columns<ColKey, Value, Timestamp>>

A wide column store's basic unit of data is a column (name/value pair). A column can be grouped in column families (analogous to a SQL table). Super column families further group column families. You can access each column independently with a row key, and columns with the same row key form a row. Each value contains a timestamp for versioning and for conflict resolution.

Google introduced Bigtable as the first wide column store, which influenced the open-source HBase often-used in the Hadoop ecosystem, and Cassandra from Facebook. Stores such as BigTable, HBase, and Cassandra maintain keys in lexicographic order, allowing efficient retrieval of selective key ranges.

Wide column stores offer high availability and high scalability. They are often used for very large data sets.

Source(s) and further reading: wide column store

• SQL & NoSQL, a brief history
• Bigtable architecture
• HBase architecture
• Cassandra architecture
• Graph database

Abstraction: graph

In a graph database, each node is a record and each arc is a relationship between two nodes. Graph databases are optimized to represent complex relationships with many foreign keys or many-to-many relationships.

Graphs databases offer high performance for data models with complex relationships, such as a social network. They are relatively new and are not yet widely-used; it might be more difficult to find development tools and resources. Many graphs can only be accessed with REST APIs .

Source(s) and further reading: graph

Source(s) and further reading: nosql.

• Explanation of base terminology
• NoSQL databases a survey and decision guidance
• Introduction to NoSQL
• NoSQL patterns

Reasons for SQL :

• Structured data
• Strict schema
• Relational data
• Need for complex joins
• Transactions
• Clear patterns for scaling
• More established: developers, community, code, tools, etc
• Lookups by index are very fast

Reasons for NoSQL :

• Semi-structured data
• Dynamic or flexible schema
• Non-relational data
• No need for complex joins
• Store many TB (or PB) of data
• Very data intensive workload
• Very high throughput for IOPS

Sample data well-suited for NoSQL:

• Rapid ingest of clickstream and log data
• Leaderboard or scoring data
• Temporary data, such as a shopping cart
• Frequently accessed ('hot') tables
• Metadata/lookup tables

Source(s) and further reading: SQL or NoSQL

• SQL vs NoSQL differences

Caching improves page load times and can reduce the load on your servers and databases. In this model, the dispatcher will first lookup if the request has been made before and try to find the previous result to return, in order to save the actual execution.

Databases often benefit from a uniform distribution of reads and writes across its partitions. Popular items can skew the distribution, causing bottlenecks. Putting a cache in front of a database can help absorb uneven loads and spikes in traffic.

Caches can be located on the client side (OS or browser), server side , or in a distinct cache layer.

CDNs are considered a type of cache.

Reverse proxies and caches such as Varnish can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers.

Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance.

In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so cache invalidation algorithms such as least recently used (LRU) can help invalidate 'cold' entries and keep 'hot' data in RAM.

Redis has the following additional features:

• Persistence option
• Built-in data structures such as sorted sets and lists

There are multiple levels you can cache that fall into two general categories: database queries and objects :

• Query-level
• Fully-formed serializable objects
• Fully-rendered HTML

Generally, you should try to avoid file-based caching, as it makes cloning and auto-scaling more difficult.

Whenever you query the database, hash the query as a key and store the result to the cache. This approach suffers from expiration issues:

• Hard to delete a cached result with complex queries
• If one piece of data changes such as a table cell, you need to delete all cached queries that might include the changed cell

See your data as an object, similar to what you do with your application code. Have your application assemble the dataset from the database into a class instance or a data structure(s):

• Remove the object from cache if its underlying data has changed
• Allows for asynchronous processing: workers assemble objects by consuming the latest cached object

Suggestions of what to cache:

• User sessions
• Fully rendered web pages
• Activity streams
• User graph data

When to update the cache

Since you can only store a limited amount of data in cache, you'll need to determine which cache update strategy works best for your use case.

The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following:

• Look for entry in cache, resulting in a cache miss
• Load entry from the database
• Add entry to cache
• Return entry

Memcached is generally used in this manner.

Subsequent reads of data added to cache are fast. Cache-aside is also referred to as lazy loading. Only requested data is cached, which avoids filling up the cache with data that isn't requested.

Disadvantage(s): cache-aside

• Each cache miss results in three trips, which can cause a noticeable delay.
• Data can become stale if it is updated in the database. This issue is mitigated by setting a time-to-live (TTL) which forces an update of the cache entry, or by using write-through.
• When a node fails, it is replaced by a new, empty node, increasing latency.

The application uses the cache as the main data store, reading and writing data to it, while the cache is responsible for reading and writing to the database:

• Application adds/updates entry in cache
• Cache synchronously writes entry to data store

Application code:

Cache code:

Write-through is a slow overall operation due to the write operation, but subsequent reads of just written data are fast. Users are generally more tolerant of latency when updating data than reading data. Data in the cache is not stale.

Disadvantage(s): write through

• When a new node is created due to failure or scaling, the new node will not cache entries until the entry is updated in the database. Cache-aside in conjunction with write through can mitigate this issue.
• Most data written might never be read, which can be minimized with a TTL.

In write-behind, the application does the following:

• Add/update entry in cache
• Asynchronously write entry to the data store, improving write performance

Disadvantage(s): write-behind

• There could be data loss if the cache goes down prior to its contents hitting the data store.
• It is more complex to implement write-behind than it is to implement cache-aside or write-through.

You can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration.

Refresh-ahead can result in reduced latency vs read-through if the cache can accurately predict which items are likely to be needed in the future.

Disadvantage(s): refresh-ahead

• Not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh-ahead.

Disadvantage(s): cache

• Need to maintain consistency between caches and the source of truth such as the database through cache invalidation .
• Cache invalidation is a difficult problem, there is additional complexity associated with when to update the cache.
• Need to make application changes such as adding Redis or memcached.
• From cache to in-memory data grid
• Scalable system design patterns
• Introduction to architecting systems for scale
• AWS ElastiCache strategies

Asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in-line. They can also help by doing time-consuming work in advance, such as periodic aggregation of data.

Message queues receive, hold, and deliver messages. If an operation is too slow to perform inline, you can use a message queue with the following workflow:

• An application publishes a job to the queue, then notifies the user of job status
• A worker picks up the job from the queue, processes it, then signals the job is complete

The user is not blocked and the job is processed in the background. During this time, the client might optionally do a small amount of processing to make it seem like the task has completed. For example, if posting a tweet, the tweet could be instantly posted to your timeline, but it could take some time before your tweet is actually delivered to all of your followers.

Redis is useful as a simple message broker but messages can be lost.

RabbitMQ is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes.

Amazon SQS is hosted but can have high latency and has the possibility of messages being delivered twice.

Tasks queues receive tasks and their related data, runs them, then delivers their results. They can support scheduling and can be used to run computationally-intensive jobs in the background.

Celery has support for scheduling and primarily has python support.

If queues start to grow significantly, the queue size can become larger than memory, resulting in cache misses, disk reads, and even slower performance. Back pressure can help by limiting the queue size, thereby maintaining a high throughput rate and good response times for jobs already in the queue. Once the queue fills up, clients get a server busy or HTTP 503 status code to try again later. Clients can retry the request at a later time, perhaps with exponential backoff .

Disadvantage(s): asynchronism

• Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity.
• It's all a numbers game
• Applying back pressure when overloaded
• Little's law
• What is the difference between a message queue and a task queue?

Communication

Hypertext transfer protocol (HTTP)

HTTP is a method for encoding and transporting data between a client and a server. It is a request/response protocol: clients issue requests and servers issue responses with relevant content and completion status info about the request. HTTP is self-contained, allowing requests and responses to flow through many intermediate routers and servers that perform load balancing, caching, encryption, and compression.

A basic HTTP request consists of a verb (method) and a resource (endpoint). Below are common HTTP verbs:

*Can be called many times without different outcomes.

HTTP is an application layer protocol relying on lower-level protocols such as TCP and UDP .

Source(s) and further reading: HTTP

• What is HTTP?
• Difference between HTTP and TCP
• Difference between PUT and PATCH

TCP is a connection-oriented protocol over an IP network . Connection is established and terminated using a handshake . All packets sent are guaranteed to reach the destination in the original order and without corruption through:

• Sequence numbers and checksum fields for each packet
• Acknowledgement packets and automatic retransmission

If the sender does not receive a correct response, it will resend the packets. If there are multiple timeouts, the connection is dropped. TCP also implements flow control and congestion control . These guarantees cause delays and generally result in less efficient transmission than UDP.

To ensure high throughput, web servers can keep a large number of TCP connections open, resulting in high memory usage. It can be expensive to have a large number of open connections between web server threads and say, a memcached server. Connection pooling can help in addition to switching to UDP where applicable.

TCP is useful for applications that require high reliability but are less time critical. Some examples include web servers, database info, SMTP, FTP, and SSH.

Use TCP over UDP when:

• You need all of the data to arrive intact
• You want to automatically make a best estimate use of the network throughput

UDP is connectionless. Datagrams (analogous to packets) are guaranteed only at the datagram level. Datagrams might reach their destination out of order or not at all. UDP does not support congestion control. Without the guarantees that TCP support, UDP is generally more efficient.

UDP can broadcast, sending datagrams to all devices on the subnet. This is useful with DHCP because the client has not yet received an IP address, thus preventing a way for TCP to stream without the IP address.

UDP is less reliable but works well in real time use cases such as VoIP, video chat, streaming, and realtime multiplayer games.

Use UDP over TCP when:

• You need the lowest latency
• Late data is worse than loss of data
• You want to implement your own error correction

Source(s) and further reading: TCP and UDP

• Networking for game programming
• Key differences between TCP and UDP protocols
• Difference between TCP and UDP
• Transmission control protocol
• User datagram protocol
• Scaling memcache at Facebook

In an RPC, a client causes a procedure to execute on a different address space, usually a remote server. The procedure is coded as if it were a local procedure call, abstracting away the details of how to communicate with the server from the client program. Remote calls are usually slower and less reliable than local calls so it is helpful to distinguish RPC calls from local calls. Popular RPC frameworks include Protobuf , Thrift , and Avro .

RPC is a request-response protocol:

• Client program - Calls the client stub procedure. The parameters are pushed onto the stack like a local procedure call.
• Client stub procedure - Marshals (packs) procedure id and arguments into a request message.
• Client communication module - OS sends the message from the client to the server.
• Server communication module - OS passes the incoming packets to the server stub procedure.
• Server stub procedure - Unmarshalls the results, calls the server procedure matching the procedure id and passes the given arguments.
• The server response repeats the steps above in reverse order.

Sample RPC calls:

RPC is focused on exposing behaviors. RPCs are often used for performance reasons with internal communications, as you can hand-craft native calls to better fit your use cases.

Choose a native library (aka SDK) when:

• You know your target platform.
• You want to control how your "logic" is accessed.
• You want to control how error control happens off your library.
• Performance and end user experience is your primary concern.

HTTP APIs following REST tend to be used more often for public APIs.

Disadvantage(s): RPC

• RPC clients become tightly coupled to the service implementation.
• A new API must be defined for every new operation or use case.
• It can be difficult to debug RPC.
• You might not be able to leverage existing technologies out of the box. For example, it might require additional effort to ensure RPC calls are properly cached on caching servers such as Squid .

REST is an architectural style enforcing a client/server model where the client acts on a set of resources managed by the server. The server provides a representation of resources and actions that can either manipulate or get a new representation of resources. All communication must be stateless and cacheable.

There are four qualities of a RESTful interface:

• Identify resources (URI in HTTP) - use the same URI regardless of any operation.
• Change with representations (Verbs in HTTP) - use verbs, headers, and body.
• Self-descriptive error message (status response in HTTP) - Use status codes, don't reinvent the wheel.
• HATEOAS (HTML interface for HTTP) - your web service should be fully accessible in a browser.

Sample REST calls:

REST is focused on exposing data. It minimizes the coupling between client/server and is often used for public HTTP APIs. REST uses a more generic and uniform method of exposing resources through URIs, representation through headers , and actions through verbs such as GET, POST, PUT, DELETE, and PATCH. Being stateless, REST is great for horizontal scaling and partitioning.

Disadvantage(s): REST

• With REST being focused on exposing data, it might not be a good fit if resources are not naturally organized or accessed in a simple hierarchy. For example, returning all updated records from the past hour matching a particular set of events is not easily expressed as a path. With REST, it is likely to be implemented with a combination of URI path, query parameters, and possibly the request body.
• REST typically relies on a few verbs (GET, POST, PUT, DELETE, and PATCH) which sometimes doesn't fit your use case. For example, moving expired documents to the archive folder might not cleanly fit within these verbs.
• Fetching complicated resources with nested hierarchies requires multiple round trips between the client and server to render single views, e.g. fetching content of a blog entry and the comments on that entry. For mobile applications operating in variable network conditions, these multiple roundtrips are highly undesirable.
• Over time, more fields might be added to an API response and older clients will receive all new data fields, even those that they do not need, as a result, it bloats the payload size and leads to larger latencies.

RPC and REST calls comparison

Source: Do you really know why you prefer REST over RPC

Source(s) and further reading: REST and RPC

• Do you really know why you prefer REST over RPC
• When are RPC-ish approaches more appropriate than REST?
• REST vs JSON-RPC
• Debunking the myths of RPC and REST
• What are the drawbacks of using REST
• Why REST for internal use and not RPC

This section could use some updates. Consider contributing !

Security is a broad topic. Unless you have considerable experience, a security background, or are applying for a position that requires knowledge of security, you probably won't need to know more than the basics:

• Encrypt in transit and at rest.
• Sanitize all user inputs or any input parameters exposed to user to prevent XSS and SQL injection .
• Use parameterized queries to prevent SQL injection.
• Use the principle of least privilege .
• API security checklist
• Security guide for developers
• OWASP top ten

You'll sometimes be asked to do 'back-of-the-envelope' estimates. For example, you might need to determine how long it will take to generate 100 image thumbnails from disk or how much memory a data structure will take. The Powers of two table and Latency numbers every programmer should know are handy references.

• Powers of two

Handy metrics based on numbers above:

• Read sequentially from HDD at 30 MB/s
• Read sequentially from 1 Gbps Ethernet at 100 MB/s
• Read sequentially from SSD at 1 GB/s
• Read sequentially from main memory at 4 GB/s
• 6-7 world-wide round trips per second
• 2,000 round trips per second within a data center

Latency numbers visualized

• Latency numbers every programmer should know - 1
• Latency numbers every programmer should know - 2
• Designs, lessons, and advice from building large distributed systems
• Software Engineering Advice from Building Large-Scale Distributed Systems

Common system design interview questions, with links to resources on how to solve each.

Articles on how real world systems are designed.

Don't focus on nitty gritty details for the following articles, instead:

• Identify shared principles, common technologies, and patterns within these articles
• Study what problems are solved by each component, where it works, where it doesn't
• Review the lessons learned

Architectures for companies you are interviewing with. Questions you encounter might be from the same domain.

• Airbnb Engineering
• Atlassian Developers
• Bitly Engineering Blog
• Cloudera Developer Blog
• Dropbox Tech Blog
• Engineering at Quora
• Ebay Tech Blog
• Evernote Tech Blog
• Etsy Code as Craft
• Facebook Engineering
• Flickr Code
• Foursquare Engineering Blog
• GitHub Engineering Blog
• Google Research Blog
• Groupon Engineering Blog
• Heroku Engineering Blog
• Hubspot Engineering Blog
• High Scalability
• Instagram Engineering
• Intel Software Blog
• Jane Street Tech Blog
• LinkedIn Engineering
• Microsoft Engineering
• Microsoft Python Engineering
• Netflix Tech Blog
• Paypal Developer Blog
• Pinterest Engineering Blog
• Reddit Blog
• Salesforce Engineering Blog
• Slack Engineering Blog
• Spotify Labs
• Twilio Engineering Blog
• Twitter Engineering
• Uber Engineering Blog
• Yahoo Engineering Blog
• Yelp Engineering Blog
• Zynga Engineering Blog

Looking to add a blog? To avoid duplicating work, consider adding your company blog to the following repo:

• kilimchoi/engineering-blogs

Interested in adding a section or helping complete one in-progress? Contribute !

• Distributed computing with MapReduce
• Scatter gather

Credits and sources are provided throughout this repo.

Special thanks to:

• Hired in tech
• Cracking the coding interview
• High scalability
• checkcheckzz/system-design-interview
• shashank88/system_design
• mmcgrana/services-engineering
• System design cheat sheet
• A distributed systems reading list
• Cracking the system design interview

Feel free to contact me to discuss any issues, questions, or comments.

My contact info can be found on my GitHub page .

I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).

Contributors 119

• Python 98.0%

System Design Tutorial

• What is System Design
• System Design Life Cycle
• High Level Design HLD
• Low Level Design LLD
• Design Patterns
• UML Diagrams
• System Design Interview Guide
• Crack System Design Round
• System Design Bootcamp
• System Design Interview Questions
• Microservices
• Scalability

System Design is the process of designing the architecture, components, and interfaces for a system so that it meets the end-user requirements. This specifically designed System Design tutorial will help you to learn and master System Design concepts in the most efficient way from basics to advanced level.

Important Topics for System Design Tutorial

Steps for approaching this system design tutorial

Basics of system design, scalability in system design, databases in designing systems, high level design(hld), low level design(lld), testing and quality assurance in system design, interview questions & answers of system design, tips for system design interview, advantages of system design, disadvantages of system design, reference books.

• Understand the requirements: Before starting the design process, it is important to understand the requirements and constraints of the system. This includes gathering information about the problem space, performance requirements, scalability needs, and security concerns.
• Identify the major components: Identify the major components of the system and how they interact with each other. This includes determining the relationships between different components and how they contribute to the overall functionality of the system.
• Choose appropriate technology: Based on the requirements and components, choose the appropriate technology to implement the system. This may involve choosing hardware and software platforms, databases, programming languages, and tools.
• Define the interface: Define the interface between different components of the system, including APIs, protocols, and data formats.
• Design the data model: Design the data model for the system, including the schema for the database, the structure of data files, and the data flow between components.
• Consider scalability and performance: Consider scalability and performance implications of the design, including factors such as load balancing, caching, and database optimization.
• Test and validate the design: Validate the design by testing the system with realistic data and use cases, and make changes as needed to address any issues that arise.
• Deploy and maintain the system: Finally, deploy the system and maintain it over time, including fixing bugs, updating components, and adding new features as needed.

By following these steps and continually refining your approach, you can develop robust and efficient system designs that meet both technical requirements and user expectations. For those looking to delve deeper and gain a competitive edge, our System Design course provides a structured pathway to mastering these essential skills.

It’s important to keep in mind that system design is an iterative process, and the design may change as new information is gathered and requirements evolve. Additionally, it’s important to communicate the design effectively to all stakeholders, including developers, users, and stakeholders, to ensure that the system meets their needs and expectations.

• What is System Design?
• Functional vs Non Functional Requirements
• What are the components of System Design?
• System Design Life Cycle | SDLC (Design)
• Structured Analysis and Structured Design (SA/SD)
• System Design Strategy
• Database Sharding – System Design Interview Concept
• System Design – Horizontal and Vertical Scaling
• Load Balancer in System Design
• Routing requests through Load Balancer
• Latency and Throughput in System Design
• Object-Oriented Analysis and Design
• Difference between Structured and Object-Oriented Analysis
• What is Scalability and How to achieve it – Learn System Design
• Which Scalability approach is right for our Application? | System Design
• Primary Bottlenecks that Hurt the Scalability of an Application | System Design
• Which Database to Choose while designing a system – SQL or NoSQL
• File and Database Storage Systems in System Design
• Database Replication in System Design
• Block, Object, and File Storage
• Normalization Process in DBMS
• Denormalization in Databases
• What is High Level Design – Learn System Design
• Availability in System Design
• Consistency in System Design
• Reliability in System Design
• CAP Theorem
• Difference between Process and Thread
• Difference between Concurrency and Parallelism
• Load Balancer
• Consistent Hashing
• Content Delivery Network(CDN) in System Design
• Caching in System Design
• Cache Eviction Policies
• Message Queues
• Communication Protocols In System Design
• Network Protocols and Proxies in System Design
• Unified Modeling Language (UML)
• What is Low Level Design or LLD
• Data Structures and Algorithms for System Design
• Event-Driven Architecture
• Difference between Authentication and Authorization
• What is API Gateway
• What is Data Encryption?
• Code Optimization Techniques
• Unit Testing
• Integration Testing
• CI/CD: Continuous Integration and Continuous Delivery
• Introduction to Modularity and Interfaces In System Design
• Data Partitioning Techniques in System Design
• Class Diagrams | UML
• Types of Software Testing
• Software Quality Assurance
• Security Testing
• Essential Security Measures in System Design
• Top 10 System Design Interview Questions and Answers
• Most Commonly Asked System Design Interview Problems/Questions
• System Design – URL Shortening Service
• Design Dropbox – A System Design Interview Question
• Design Twitter – A System Design Interview Question
• System Design Netflix – A Complete Architecture
• System Design of Uber App – Uber System Architecture
• Design BookMyShow – A System Design Interview Question
• Designing Facebook Messenger | System Design Interview
• Designing Whatsapp Messenger | System Design
• Designing Instagram | System Design
• How to Crack System Design Round in Interviews?
• 5 Tips to Crack Low-Level System Design Interviews
• 5 Common System Design Concepts for Interview Preparation
• 6 Steps To Approach Object-Oriented Design Questions in Interview
• Better understanding of requirements: System design helps to clarify the requirements and constraints of a system, which can lead to a better understanding of the problem space.
• Improved efficiency: By designing a system with appropriate technology and optimized data structures, system design can improve the efficiency and performance of a system.
• Better scalability: System design can help ensure that a system is scalable and can accommodate future growth and changing requirements.
• Improved maintainability: By defining clear interfaces and data models, system design can improve the maintainability of a system and make it easier to update and modify over time.
• Better communication: System design helps to communicate the design of a system to stakeholders, including developers and users, which can help ensure that the system meets their needs and expectations.
• Time-consuming: The process of system design can be time-consuming, especially for large and complex systems.
• Cost: System design can be expensive, especially if it involves significant research, prototyping, and testing.
• Requires expertise: System design requires a significant amount of technical expertise, including knowledge of hardware, software, databases, and data structures.
• Limited flexibility: Once a system design is complete, it can be difficult to make changes to the design, especially if the design has already been implemented and deployed.
• “Design Patterns: Elements of Reusable Object-Oriented Software” by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides
• “The Design of Everyday Things” by Don Norman
• “Systems Analysis and Design” by Alan Dennis and Barbara Haley Wixom
• “Clean Architecture: A Craftsman’s Guide to Software Structure and Design” by Robert C. Martin
• “Software Architecture in Practice” by Len Bass, Paul Clements, and Rick Kazman

Some other important Tutorials:

• DSA Tutorial
• Software Development Roadmap
• Roadmap to become a Product Manager

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Why should I learn system design?

Over the last two decades, there have been a lot of advancements in large-scale web applications. These advancements have redefined the way we think about software development. All of the apps and services that we use daily, like Facebook, Instagram, and Twitter, are scalable systems. Billions of people worldwide access these systems concurrently, so they need to be designed to handle large amounts of traffic and data. This is where system design comes in.

As a software developer, you’ll be increasingly expected to understand system design concepts and how to apply them. In the early stages of your career, learning system design will allow you to tackle software design problems with more confidence and apply design principles to your everyday work. As you progress through your career and begin interviewing for higher-level positions, system design will become a larger part of your interview process. So, no matter what your level is, system design matters to you. 

Because of its increasing importance, we wanted to create a resource to help you navigate the world of system design. This guide details the fundamental concepts of system design and also links you to relevant resources to help you gain a deeper understanding and acquire real-world, hands-on experience.

This guide covers :

What is System Design?

Horizontal and vertical scaling, microservices, proxy servers, cap theorem, redundancy and replication, block storage, file storage, object storage, redundant disk arrays (raid), google file system (gfs), hadoop distributed file system (hdfs), bloom filters, consistent hashing, merkle trees, leader election, how to choose a database, database schemas, database queries, acid properties, database sharding and partitioning, database indexing, what are distributed systems, distributed system failures, stateless and stateful systems.

• Distibuted system design patterns

DNS and load balancing

N-tier applications, http and rest, stream processing, cache invalidation, cache eviction, machine learning and system design, containerization and system design.

• The cloud and System Design

For a guide specific to the System Design Interview, check out our popular course: Grokking the Modern System Design Interview for Engineers & Managers .

System design is the process of defining the architecture, interfaces, and data for a system that satisfies specific requirements. System design meets the needs of your business or organization through coherent and efficient systems. Once your business or organization determines its requirements, you can begin to build them into a physical system design that addresses the needs of your customers. The way you design your system will depend on whether you want to go for custom development, commercial solutions, or a combination of the two.

System design requires a systematic approach to building and engineering systems. A good system design requires you to think about everything in an infrastructure, from the hardware and software, all the way down to the data and how it’s stored.

Learn more about the basics of system design .

System Design fundamentals

Scalability refers to an application’s ability to handle and withstand an increased workload without sacrificing latency . An application needs solid computing power to scale well. The servers should be powerful enough to handle increased traffic loads. There are two main ways to scale an application: horizontally and vertically.

Horizontal scaling, or scaling out , means adding more hardware to the existing hardware resource pool. It increases the computational power of the system as a whole. Vertical scaling, or scaling up , means adding more power to your server. It increases the power of the hardware running the application.

There are pros and cons to both types of scaling. You’ll come across scenarios where you need to consider the tradeoffs and decide which type of scaling is best for your use case. To dive deeper into scaling, check out the scalability module in our hands-on course Web Application and Software Architecture 101 . You’ll learn about the benefits and risks of scaling, primary bottlenecks that hurt application scalability, and more.

Microservices , or microservice architecture , is an architectural style that structures an application using loosely coupled services . It divides a large application into a collection of separate, modular services. These modules can be independently developed, deployed, and maintained.

Microservices operate at a much faster and more reliable speed than traditional monolithic applications. Since the application is broken down into independent services, every service has its own logic and codebase. These services can communicate with one another through Application Programming Interfaces (APIs).

Microservices are ideal if you want to develop a more scalable application. With microservices, it’s much easier to scale your applications because of their modern capabilities and modules. If you work with a large or growing organization, microservices are great for your team because they’re easier to scale and customize over time. To learn more about microservices and their benefits, drawbacks, technology stacks, and more, check out this microservices architecture tutorial.

A proxy server , or forward proxy , acts as a channel between a user and the internet . It separates the end-user from the website they’re browsing. Proxy servers not only forward user requests but also provide many benefits, such as:

• Improved security
• Improved privacy
• Access to blocked resources
• Control of the internet usage of employees and children
• Cache data to speed up requests

Whenever a user sends a request for an address from the end server, the traffic flows through a proxy server on its way to the address. When the request comes back to the user, it flows back through the same proxy server which then forwards it to the user.

The CAP theorem is a fundamental theorem within the field of system design. It states that a distributed system can only provide two of three properties simultaneously: consistency, availability, and partition tolerance. The theorem formalizes the tradeoff between consistency and availability when there’s a partition.

Learn more about CAP theorem .

Redundancy is the process of duplicating critical components of a system with the intention of increasing a system’s reliability or overall performance. It usually comes in the form of a backup or fail-safe. Redundancy plays a critical role in removing single points of failure in a system and providing backups when needed. For example, if we have two instances of a service running in production and one of those instances fails, the system can fail over to another one.

Replication is the process of sharing information to ensure consistency between redundant resources . You can replicate software or hardware components to improve reliability, fault-tolerance, or accessibility. Replication is used in many database management systems (DBMS), typically with a primary-replica relationship between the original and its copies. The primary server receives all of the updates, and those updates pass through the replica servers. Each replica server outputs a message when it successfully receives the update.

Learn more about redundancy and replication in the Web Application & Software Architecture module of Scalability & System Design for Developers .

Data is at the center of every system. When designing a system, we need to consider how we’re going to store our data. There are various storage techniques that we can implement depending on the needs of our system.

Block storage is a data storage technique where data is broken down into blocks of equal sizes , and each individual block is given a unique identifier for easy accessibility. These blocks are stored in physical storage. As opposed to adhering to a fixed path, blocks can be stored anywhere in the system, making more efficient use of the resources.

Learn more about block storage .

File storage is a hierarchical storage methodology . With this method, the data is stored in files. The files are stored in folders, which are then stored in directories. This storage method is only good for a limited amount of data, primarily structured data.

As the size of the data grows beyond a certain point, this data storage method can become a hassle.

Learn more about file storage .

Object storage is the storage designed to handle large amounts of unstructured data . Object storage is the preferred data storage method for data archiving and data backups because it offers dynamic scalability . Object storage isn’t directly accessible to an operating system. Communication happens through RESTful APIs at the application level. This type of storage provides immense flexibility and value to systems, because backups, unstructured data, and log files are important to any system. If you’re designing a system with large datasets, object storage would work well for your organization.

Learn more about object storage .

A redundant array of inexpensive disks, or RAID, is a technique to use multiple disks in concert to build a faster, bigger, and more reliable disk system. Externally, a RAID looks like a disk. Internally, it’s a complex tool, consisting of multiple disks, memory, and one or more processors to manage the system. A hardware RAID is similar to a computer system but is specialized for the task of managing a group of disks. There are different levels of RAID, all of which offer different functionalities. When designing a complex system, you may want to implement RAID storage techniques.

Learn more about the advantages and disadvantages of RAID .

• Message queues

A message queue is a queue that routes messages from a source to a destination , or from the sender to the receiver. It follows the FIFO (first in first out) policy. The message that is sent first is delivered first. Message queues facilitate asynchronous behavior , which allows modules to communicate with each other in the background without hindering primary tasks. They also facilitate cross-module communication and provide temporary storage for messages until they are processed and consumed by the consumer.

Learn more about message queues in the Web Application & Software Architecture module of Scalability & System Design for Developers .

Apache Kafka started in 2011 as a messaging system for LinkedIn but has since grown to become a popular distributed event streaming platform . The platform is capable of handling trillions of records per day. Kafka is a distributed system consisting of servers and clients that communicate through a TCP network protocol. The system allows us to read, write, store, and process events. Kafka is primarily used for building data pipelines and implementing streaming solutions.

While Kafka is a popular messaging queue option, there are other popular options as well. To learn more about which messaging queue to use, we recommend the following resources:

• Kafka vs RabbitMQ
• Kafka vs Kinesis
• Kafka vs Flink

File systems

File systems are processes that manage how and where data on a storage disk is stored . It manages the internal operations of the storage disk and explains how users or applications can access disk data. File systems manage multiple operations, such as:

• File naming
• Storage management
• Directories
• Access rules

Without file systems, it would be hard to identify files, retrieve files, or manage authorizations for individual files.

Google File System (GFS) is a scalable distributed file system designed for large data-intensive applications , like Gmail or YouTube. It was built to handle batch processing on large data sets. GFS is designed for system-to-system interaction, rather than user-to-user interaction. It’s scalable and fault-tolerant. The architecture consists of GFS clusters, which contain a single master and multiple ChunkServers that can be accessed by multiple clients.

It’s common to be asked to design a distributed file system, such as GFS, in system design interviews . To prepare for this xview question, check out the System Design Interview resources in Grokking Modern System Design for Engineers & Managers .

The Hadoop Distributed File System (HDFS) is a distributed file system that handles large sets of data and runs on commodity hardware. It was built to store unstructured data. HDFS is a more simplified version of GFS. A lot of its architectural decisions are inspired by the GFS design. HDFS is built around the idea that the most efficient data processing pattern is a “write once, read many times” pattern.

It’s common to be asked to design a distributed file storage system, such as HDFS, in system design interviews. To prepare for this system design interview question , check out our Grokking Modern System Design for Engineers and Managers course.

System Design patterns

Knowing system design patterns is very important because they can be applied to all types of distributed systems. They also play a major role in system design interviews. System design patterns refer to common design problems related to distributed systems and their solutions. Let’s take a look at some commonly used patterns.

Bloom filters are probabilistic data structures designed to answer the set membership question: Is this element present in the set?

Bloom filters are highly space-efficient and do not store actual items. They determine whether an item does not exist in a set or if an item might exist in a set. They can’t tell if an item is definitely present in a set. An empty Bloom filter is a bit vector with all bits set to zero. In the graphic below, each cell represents a bit. The number below the bit is its index in a 10-bit vector.

Consistent hashing maps data to physical nodes and ensures that only a small set of keys move when servers are added or removed. Consistent hashing stores the data managed by a distributed system in a ring. Each node in the ring is assigned a range of data. This concept is important within distributed systems and works closely with data partitioning and data replication. Consistent hashing also comes up in system design interviews.

Learn more about consistent hashing .

A quorum is the minimum number of servers on which a distributed operation needs to be performed successfully before declaring the operation’s overall success.

When moving data between components in a distributed system, it’s possible that the data fetched from a node may arrive corrupted. This corruption occurs because of faults in storage devices, networks, software, etc. When a system is storing data, it computes a checksum of the data and stores the checksum with the data. When a client retrieves data, it verifies that the data received from the server matches the stored checksum.

Learn more about Checksum .

A Merkle tree is a binary tree of hashes , in which each internal node is the hash of its two children, and each leaf node is a hash of a portion of the original data. Replicas can contain a lot of data. Splitting up the entire range to calculate checksums for comparison is not very feasible, because there’s so much data to be transferred. Merkle trees enable us to easily compare replicas of a range.

Learn more about Merkle trees .

Leader election is the process of designating a single process as the organizer of tasks distributed across several computers. It’s an algorithm that enables each node throughout the network to recognize a particular node as the task leader. Network nodes communicate with each other to determine which of them will be the leader. Leader election improves efficiency, simplifies architectures, and reduces operations.

Learn more about leader election .

Relational databases

Relational databases, or SQL databases , are structured . They have predefined schemas , just like phone books that store numbers and addresses. SQL databases store data in rows and columns. Each row contains all of the information available about a single entity, and each column holds all of the separate data points. Popular SQL databases include:

• MS SQL Server

MySQL is an open-source relational database management system (RDBMS) that stores data in tables and rows. It uses SQL (structured query language) to transfer and access data, and it uses SQL joins to simplify querying and correlation. It follows client-server architecture and supports multithreading .

PostgreSQL, also known as Postgres, is an open-source RDBMS that emphasizes extensibility and SQL compliance. Postgres employs SQL to access and manipulate the database. It uses its own version of SQL called PL/pgSQL, which can perform more complex queries than SQL. Postgres transactions follow the ACID principle. Because it has a relational structure, the whole schema needs to be designed and configured at the time of creation. Postgres databases use foreign keys, which allow us to keep our data normalized.

SQL joins allow us to access information from two or more tables at once . They also keep our databases normalized, which ensures that data redundancy is low. When data redundancy is low, we can decrease the amount of data anomalies in our application when we delete or update records.

Non-relational databases

Non-relational databases, or no-SQL databases , are unstructured . They have a dynamic schema , like file folders that store information from someone’s address and number to their Facebook likes and online shopping preferences. There are different types of NoSQL. The most common types include:

• Key-value stores, such as Redis and DynamoDB
• Document databases, such as MongoDB and CouchDB
• Wide-column databases, such as Cassandra and HBase
• Graph databases, such as Neo4J and InfiniteGraph

MongoDB is a NoSQL, non-relational database management system (DBMS) that uses documents instead of tables or rows for data storage. This data model makes it possible to manipulate related data in a single database operation. MongoDB documents use JSON-like documents and files that are JavaScript supported. The document fields can vary, making it easy to change the structure over time.

Databases are a basic foundation of software development. They serve many different purposes for building projects of all sizes and types. When choosing your database structure, it’s important to factor in speed, reliability, and accuracy . We have relational databases that can guarantee data validity, and we have non-relational databases that can guarantee eventual consistency. When choosing your database structure, it’s important to factor in database fundamentals, such as:

• Normalization
• Persistence

Database decisions are an important part of system design interviews , so it’s important to get comfortable with making decisions based on unique use cases. The database you choose will depend upon your project. To learn more about how to choose the right database for your project, we recommend the following resources:

• MongoDB vs PostgreSQL
• MongoDB vs MySQL
• SQL vs. NoSQL

Database schemas are abstract designs that represent the storage of the data in a database . They describe the organization of data and the relationships between tables in a given database. You plan database schemas in advance so you know what components are necessary and how they’ll connect to each other. A database schema doesn’t hold data but instead describes the shape of the data and how it relates to other tables or models. An entry in a database is an instance of a database schema.

There are two main database schema types that define different parts of the schema: logical and physical .

Database schemas include:

• All important or relevant data
• Consistent formatting for all data entries
• Unique keys for all entries and database objects
• Each column in a table has a name and a data type

The size and complexity of a database schema depend on the size of the project. The visual style of database schemas allows you to properly structure your database and its relationships before jumping into the code. The process of planning a database design is called data modeling . Database schemas are important for designing DBMS and RDBMS.

A database query is a request to access data from a database to manipulate or retrieve it . It’s most closely associated with CRUD operations . Database queries allow us to perform logic with the information we get in response to the query. There are many different approaches to queries, from using query strings to writing with a query language, to using a QBE (Query by Example) like GraphQL.

To maintain the integrity of a database , all transactions must obey ACID properties . ACID is an acronym that stands for atomicity, consistency, isolation, and durability.

• Atomicity : A transaction is an atomic unit. All of the instructions within a transaction will successfully execute, or none of them will execute.
• Consistency : A database is initially in a consistent state, and it should remain consistent after every transaction.
• Isolation : If multiple transactions are running concurrently, they shouldn’t be affected by each other, meaning that the result should be the same as the result obtained if the transactions were running sequentially.
• Durability : Changes that have been committed to the database should remain, even in the event of a software or system failure.

When sharding a database , you make partitions of data so that the data is divided into various smaller, distinct chunks called shards. Each shard could be a table, a Postgres schema, or a different physical database held on a separate database server instance. Some data within the database remains present in all shards, while some only appear in single shards. These two situations can be referred to as vertical sharding and horizontal sharding. Let’s take a look at a visual:

To shard your data, you need to determine a sharding key to partition your data. The sharding key can either be an indexed field or indexed compound fields that exist in every document in the collection. There’s no general rule for determining your sharding key. It all depends on your application.

Sharding allows your application to make fewer queries. When it receives a request, the application knows where to route the request. This means that it has to look through less data rather than going through the entire database. Sharding improves your application’s overall performance and scalability.

Data partitioning is a technique that breaks up a big database into smaller parts. This process allows us to split our database across multiple machines to improve our application’s performance, availability, load balancing, and manageability.

Database indexing allows you to make it faster and easier to search through your tables and find the rows or columns that you want. Indexes can be created using one or more columns of a database table, providing the basis for both rapid random lookups and efficient access of ordered information. While indexes dramatically speed up data retrieval, they typically slow down data insertion and updates because of their size.

Learn more about databases for system design in Grokking Modern System Design for Engineers and Managers .

Distributed systems make it easier for us to scale our applications at exponential rates . Many top tech companies use complex distributed systems to handle billions of requests and perform updates without downtime. A distributed system is a collection of computers that work together to form a single computer for the end user. All of the computers in the collection share the same state and operate concurrently. These machines can also fail independently without affecting the entire system.

Distributed systems can be difficult to deploy and maintain, but they provide many benefits, including:

• Scaling : Distributed systems allow you to scale horizontally to account for more traffic.
• Modular growth : There’s almost no cap on how much you can scale.
• Fault tolerance : Distributed systems are more fault-tolerant than single machines.
• Cost-effective : The initial cost is higher than traditional systems, but because of their capacity to scale, they quickly become more cost-effective.
• Low latency : You can have a node in multiple locations, so traffic will hit the closest node.
• Efficiency : Distributed systems break complex data into smaller pieces.
• Parallelism : Distributed systems can be designed for parallelism, where multiple processors divide up a complex problem into smaller chunks.

A distributed system can encounter several types of failures. Four basic types of failures include:

System failure

System failures occur because of software or hardware failures. System failures usually result in the loss of the contents of the primary memory, but the secondary memory remains safe. Whenever there’s a system failure, the processor fails to perform the execution, and the system may reboot or freeze.

Communication medium failure

Communication medium failures occur as a result of communication link failures or the shifting of nodes.

Secondary storage failure

A secondary storage failure occurs when the information on the secondary storage device is inaccessible. It can be the result of many different things, including node crashing, dirt on the medium, and parity errors.

Method failure

Method failures usually halt the distributed system and make it unable to perform any executions at all. A system may enter a deadlock state or do protection violations during method failures.

Distributed system fundamentals

In this section, we’ll cover some fundamental concepts within the field of distributed systems.

For a more comprehensive list, we recommend the following resource: Distributed Systems for Practitioners . This course establishes the basic, fundamental principles of distributed systems, and it provides many additional resources for those who want to invest more time in gaining a deeper understanding.

MapReduce is a framework developed by Google to handle large amounts of data in an efficient manner. MapReduce uses numerous servers for data management and distribution. The framework provides abstractions to underlying processes happening during the execution of user commands. A few of these processes include fault tolerance, partitioning data, and aggregating data. The abstractions allow the user to focus on the higher-level logic of their programs while trusting the framework to smoothly continue underlying processes.

The MapReduce workflow is as follows:

Partitioning : The data is usually in the form of a big chunk. It’s necessary to begin by partitioning the data into smaller, more manageable pieces that can be efficiently handled by the map workers.

Map : Map workers receive the data in the form of a key-value pair. This data is processed by the map workers, according to the user-defined map function, to generate intermediate key-value pairs.

Intermediate files : The data is partitioned into R partitions (with R being the number of reduce workers). These files are buffered in the memory until the primary node forwards them to the reduce workers.

Reduce : As soon as the reduce workers get the data stored in the buffer, they sort it accordingly and group data with the same keys.

Aggregate : The primary node is notified when the reduce workers are done with their tasks. In the end, the sorted data is aggregated together and R output files are generated for the user.

Learn more about MapReduce .

Stateless and stateful systems are important concepts in the world of distributed systems. A system is either stateless or stateful. A stateless system maintains no state of past events . It executes based on the inputs we provide to it. Stateful systems are responsible for maintaining and mutating a state . To learn more about stateless vs. stateful systems and the advantages of stateless systems, check out the following resource: Distributed Systems for Practitioners .

Raft establishes the concept of a replicated state machine and the associated replicated log of commands as first-class citizens and supports multiple consecutive rounds of consensus by default. It requires a set of nodes that form a consensus group, or a Raft cluster. Each of these can be in one of these three states:

Learn more about Raft nodes and the implementation of Raft .

Distributed system design patterns

Design patterns give us ways to build systems that fit particular use cases. They are like building blocks that allow us to pull from existing knowledge rather than start every system from scratch. They also create a set of standard models for system design that help other developers see how their projects can interface with a given system.

Creational design patterns provide a baseline when building new objects. Structural patterns define the overall structure of a solution. Behavioral patterns describe objects and how they communicate with each other. Distributed system design patterns outline a software architecture for how different nodes communicate with each other, which nodes handle particular tasks, and what the process flows should look like for various tasks.

Most distributed system design patterns fall into one of three categories based on the functionality they work with:

Object communication : Describes the messaging protocols and permissions for different components of the system to communicate

Security : Handles confidentiality, integrity, and availability concerns to ensure the system is secure from unauthorized access

Event-driven : Describes the production, detection, consumption, and response to system events

Learn the top 5 distributed system design patterns .

Scalable web applications

DNS, or domain name system , averts the need to remember long IP addresses to visit websites by mapping simple domain names to IP addresses . You can set up DNS load balancing for large-scale applications and systems that need to spread user traffic across different clusters in different data centers.

Load balancing is very important to our scaling efforts. It allows us to scale effectively with increases in traffic and stay highly available . Load balancing is executed by load balancers , which are devices that act as reverse proxies. They’re responsible for distributing network traffic across multiple servers using different algorithms. The distribution of traffic helps avert the risks of all the traffic converging to a single machine or just a couple of machines in the cluster. If the traffic converges to only a couple of machines, this will overload them and bring them down.

Load balancing helps us avoid these problems. If a server goes down while the application is processing a user request, the load balancer automatically routes future requests to servers that are functioning.

Learn more about load balancing .

N-tier applications, or distributed applications , are applications that have more than three components involved . Those components can be:

• Load balancers
• Search servers
• Components involved in processing large amounts of data
• Components running heterogeneous tech, commonly known as web services

Large applications, such as Instagram, Facebook, and Uber, are n-tier applications.

Learn more about tiered applications .

HTTP stands for HyperText Transfer Protocol. This protocol dictates the format of messages, how and when messages are sent, appropriate responses, and how messages are interpreted . HTTP messages can be either requests or responses . HTTP APIs expose endpoints as API gateways for HTTP requests to have access to servers. They come in various forms based on their target use cases, and they can be further categorized by the architectural design principles used when they’re created.

REST stands for Representational State Transfer. It’s a software architectural style for implementing web services. REST is a ruleset that defines best practices for sharing data between clients and servers , and it emphasizes the scalability of components and the simplicity of interfaces. REST applications use HTTP methods, such as GET , POST , DELETE , and PUT .

REST APIs are API implementations that adhere to REST architectural principles. They act as interfaces where the communication between clients and servers happens over HTTP . REST APIs take advantage of HTTP methodologies to establish communication between clients and servers. REST also enables servers to cache responses that improve application performance.

HTTP and REST are important concepts and considerations for client-server communication in system design.

Learn more about HTTP and REST .

Stream processing refers to a computer programming architecture that focuses on the real-time processing of continuous streams of data . Popular stream processing tools include Kafka, Storm , and Flink.

Learn more about stream processing .

A cache is hardware or software that you use to temporarily store data so it can be accessed quickly . Caches are typically very small, which makes them cost-effective and efficient. They’re used by cache clients, such as web browsers, CPUs, operating systems, and DNS servers. Accessing data from a cache is a lot faster than accessing it from the main memory or any other type of storage.

What is caching? How does it work?

Let’s say that a client wants to access some data. First, the client can check if the data is stored in the cache. If they find the data, it will immediately be returned to the client. This is called a cache hit . If the data isn’t stored in the cache, a cache miss occurs. When this happens, the client obtains data from the main memory and stores it in the cache.

There are different types of caching strategies :

Cache invalidation is a process where a computer system declares cache entries as “invalid” and either removes or replaces them. The basic objective of this process is to ensure that when the client requests the affected content, the latest version is returned. There are three defined cache invalidation schemes:

Write-through cache

Write-around cache

Write-back cache

Learn more about cache invalidation .

If a cache has space, data will be easily inserted. If a cache is full, some data will be evicted. What gets evicted, and why, depends on the eviction policy used. Some commonly used cache eviction policies include:

First in first out (FIFO) : The cache evicts the first block accessed first without any regard to how often or how many times it was accessed before.

Last in first out (LIFO) : The cache evicts the block accessed most recently first without any regard to how often or how many times it was accessed before.

Least recently used (LRU) : The cache evicts the least recently used items first.

Most recently used (MRU) : The cache evicts the most recently used items first.

Least frequently used (LFU) : The cache counts how often an item is needed. The items that are used least frequently are evicted first.

Random replacement (RR) : The cache randomly selects a candidate and evicts it.

Learn more about cache eviction .

Machine learning (ML) applications and systems are increasing in popularity and are becoming more widely used throughout various industries. As these ML applications and systems continue to mature and expand, we need to begin thinking more deeply about how we design and build them. Machine learning system design is the process of defining the software architecture, algorithms, infrastructure, and data for machine learning systems to satisfy specific requirements.

If you want to be a machine learning engineer, you’ll be expected to have solid engineering foundations and hands-on ML experiences. ML interviews share similar components to traditional coding interviews. You’ll go through a similar method of problem-solving to answer questions about system design, machine learning, and machine learning system design.

The standard development cycle of machine learning includes data collection, problem, formulation, model creation, implementation of models, and enhancement of models. There are no common, standardized guidelines for approaching machine learning system design from end to end. However, we have a resource to help you approach ML system design with a top-down approach: Machine Learning System Design .

Containerization is the packaging of software code with its dependencies to create a “container” that can run on any infrastructure. We can think of containers as more lightweight versions of virtual machines (VMs) that don’t need their own operating system. All containers on a host share that host’s operating system, which frees up a lot of system resources. Containerization wasn’t very accessible until Docker came along. Docker is an open-source containerization platform that we can use to build and run containers. Docker containers create an abstraction layer at the application layer.

Docker often gets confused with Kubernetes, which is another popular containerization tool. The two technologies complement each other and are frequently used together. While Docker is a containerization platform, Kubernetes is a containerization software that allows us to control and manage containers and VMs. With Kubernetes, you can run Docker containers and manage your containerized applications. Containers are grouped into pods, and those pods can be scaled and managed however you want. To dive deeper into these two technologies, we recommend this resource: Docker vs Kubernetes .

Similar to ML technologies, containerization technologies are also growing in popularity and are becoming more widespread across various industries. Because of this, the design and implementation of containerization systems are also gaining ground.

Here are a few containerization resources to help you further your learning:

• Docker for Developers
• DevOps for Developers
• Quick Start Kubernetes

The Cloud and System Design

Cloud computing allows access to services like storage or development platforms on-demand via internet-connected offsite data centers . Data centers are managed by third-party companies, or cloud providers . The cloud computing model not only addresses the problems that occur with on-premises systems, but also is more cost-effective, scalable, and convenient.

Different cloud providers offer different cloud services, such as storage, security, access management, and more. Cloud services give you the tools to be able to design and implement flexible and efficient systems. Cloud services vary in size and complexity, and there are various cloud deployment models that you can leverage. Different cloud system architectures include:

• Multi-cloud
• Hybrid cloud
• Single cloud
• Public cloud
• Private cloud

Cloud computing continues to grow in popularity. It can play a major role in system design and architecture . It’s important to know about these services and models when designing a system.

Here’s are a few cloud resources to help further your learning:

• Cloud Architecture: A Guide to Design & Architect your Cloud
• Cloud Computing 101: Master the Fundamentals
• Scalability & System Design for Developers

Continue learning about System Design

Grokking Modern System Design for Engineers & Managers

The complete guide to the System Design Interview

How to prepare for the System Design Interview

Frequently Asked Questions

How many months does it take to learn system design?

It is advised that you should dedicate 1–3 months to consistent study and practice. Aim at understanding various system architectures and engaging with increasingly intricate design challenges.

How to start the system design prep?

To start the system design prep, focus on the following fundamentals:

• Understand the core concepts: Study key principles like scalability, load balancing, caching, databases, APIs, microservices, and security.
• Practice with real-world problems: Solve design problems and case studies to apply concepts.
• Review architecture patterns: Familiarize yourself with common design patterns and their applications.
• Take up a few system design courses: The ‘Grokking the system design for manager’ course is highly recommended.
• Participate in discussions: Join forums, read blogs, and discuss with peers to broaden your understanding.

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Introduction To System Analysis and Design

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Presentation on theme: "Introduction To System Analysis and Design"— Presentation transcript:

Systems Analysis and Design Third Edition

Systems Analysis and Design With UML 2

Chapter 2 Approaches to System Development

Slide 1 INTRODUCTION Chapter 1. Slide 2 Key Ideas The primarily goal of a system is to create value for the organization. Many failed systems were abandoned.

Systems Development Life Cycle:

System Analysis and Design (SAD )

Slide 1 Systems Analysis & Design CS183 Spring Semester 2008 Dr. Jonathan Y. Clark Course Website:

Slide 1 INTRODUCTION Chapter 1. Slide 2 Key Ideas Many failed systems were abandoned because analysts tried to build wonderful systems without understanding.

IS 421 Information Systems Management James Nowotarski 16 September 2002.

Systems Analysis and Design in a Changing World, Fifth Edition

DECISION SUPPORT SYSTEM DEVELOPMENT

Introduction To System Analysis and design

PowerPoint Presentation for Dennis, Wixom, & Tegarden Systems Analysis and Design with UML, 3rd Edition Copyright © 2009 John Wiley & Sons, Inc. All rights.

Chapter 2: Approaches to System Development

Slide 1 Systems Analysis and Design With UML 2.0 An Object-Oriented Approach, Second Edition Chapter 1: Introduction to Systems Analysis and Design Alan.

Systems Analysis and Design CHAPTER 1

PowerPoint Presentation for Dennis & Haley Wixom, Systems Analysis and Design, 2 nd Edition Copyright 2003 © John Wiley & Sons, Inc. All rights reserved.

Chapter 1: Introduction to Systems Analysis and Design

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Systems Analysis and Design

Mar 15, 2019

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Systems Analysis and Design. Objectives. Define the terms system, analysis, and design Describe the principal functions of the systems analyst List and describe the phases of the systems development life cycle Describe the various data gathering and analysis tools

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Objectives • Define the terms system, analysis, and design • Describe the principal functions of the systems analyst • List and describe the phases of the systems development life cycle • Describe the various data gathering and analysis tools • Describe a selection of systems design tools • Explain the concept of prototyping • Explain the role of the maintenance task in the systems development life cycle

Contents • What is Systems Analysis and Design? • Systems Analyst • Systems Development Life Cycle • Preliminary Investigation • Analysis • Design • Development • Implementation

Systems Analysis and Design What is it? • System • organized set of related components established to accomplish certain task • Natural • Planned and placed by people • Computer system • A system that has computers as one of its components

Systems Analysis and Design What is it? • Systems analysis Process of studying an existing system to determine how it works and how it meets user needs • Systems design Process of developing a plan for an improved system, based upon the results of the systems analysis

Systems Analysis and Design Why do it? • Impetus for change • Internal force • External force • Authority for change • Approval of higher management

Systems Analyst • Professional computer employee who performs analysis and design • Change agent • Overcome reluctance of users to change • Typical career path • Programmer • Programmer / Analyst • Systems Analyst

Systems AnalystFunctions Coordination • Schedules and system-related tasks • Personnel • Manager • Programmers • Users • Vendors of computer equipment • Contractors

Systems AnalystFunctions Communication • Oral presentations • Written documentation Planning and design • Plans and designs new system • Involved from beginning of project through final implementation of the system

Systems AnalystPersonal Qualities • Analytical mind • Good communication skills • Self-discipline • Self-direction • Organizational skills • Creativity • Ability to work without tangible results

SDLCSystems Development Life Cycle • Preliminary investigation • Analysis • Design • Development • Implementation

SDLCPreliminary Investigation • Feasibility study / System survey • Determine the problem • Describe the problem • Understand management decisions • Organizational chart • Informal hierarchy • Produces rough plan and what to do

SDLCPreliminary Investigation Problem Definition • Nature of the problem Separate problem from symptoms of problem • Scope of the project Budget and schedule • Objectives of the project What user thinks system should do

SDLCPreliminary Investigation Report What you have found Recommendations Financially feasible

SDLCPreliminary Investigation Resulting Management Decision • Drop • Fix a simple problem • Authorize the analysis phase

SDLCAnalysis • Understand the existing system • Gather data • Analyze data • Establish system requirements

SDLCAnalysis – Data Gathering • Written documents • Interviews • Structured • Unstructured • Questionnaires • Observation • Visits by appointment • Participant observation • Sampling

SDLCAnalysis – Analyze Data • How the current system works • Determine system requirements • Basis for documentation • Tools • Data flow diagram (DFD) • Decision tables

SDLCAnalysis – Data Flow Diagram

SDLCAnalysis –Decision Tables

SDLCAnalysis – System Requirements • Detailed list of things the system must be able to do • Design is based upon system requirements • Agreement upon requirements is needed before proceeding

SDLCAnalysis – Report to Management • Summarize problems • Describe requirements • Cost analysis • Recommendations for next step • Obtain authorization to proceed

SDLCDesign • Planning the new system • Two phases • Preliminary design • Detail design

SDLCPreliminary Design Major system aspects • Centralized or distributed • Online or batch • PC-based? • How will input be captured? • Necessary reports

SDLCPreliminary Design • Make or buy decision • Packaged software • Meet at least 75% of requirements? • Change business procedures for part or all of remainder? • Customize for part of all of remainder? • Custom software • Programmers write code • Outsourcing • System is developed by external organization

SDLCPreliminary Design • Create an overall plan • Offer alternatives that meet requirements • Explain differences • Evaluate costs

SDLCPreliminary Design • Build a prototype • Limited working system of subset • Does not need true functionality • Output looks like anticipated system output • Working model that can be modified and fine-tuned • Uses high-level software tools – CASE • Best for small-scale systems

SDLCPreliminary Design CASE tools Computer-Aided Software Engineering • Supports specific analysis and design tasks • Integrated environment that supports the entire systems development process

SDLCPreliminary Design Presentation • All alternatives • Selected plan • Prototype of the system • Obtain authorization to proceed

SDLCDetail Design Parts of detail design phase • Output requirements • Input requirements • Files and databases • Systems processing • Systems controls and backup

SDLCDetail Design Output requirements • Medium • Type of reports • Contents

SDLCDetail Design

SDLCDetail Design Input requirements • Medium • Content • Input forms • Validation • Volume

SDLCDetail Design Files and Databases • Organization • Access • Format of records • Coordinate with database administrator regarding external databases and updating

SDLCDetail Design Flowchart Symbols

SDLCDetail Design Systems Controls and Backup • Insure that input is processed correctly • Prevent fraud and tampering • System journals • Backup of system files

SDLCDetail Design Report to Management • Detailed design specifications report • Presentation • Obtain authorization to proceed

SDLCDevelopment • Doing the work to bring the new system into being • Scheduling

SDLCDevelopment • Programming • Refine the design • Detailed logic flowcharts and pseudocode • Testing • Unit testing • System testing • Volume testing

SDLCImplementation • Converting to the new system • Training • Equipment conversion • File conversion • System conversion • Auditing • Evaluation • Maintenance

SDLCImplementation – Training • Begin during testing • User’s manual (Technical Writers) • Hands-on • Training consideration • Space • Equipment • Data • User’s schedules

SDLCImplementation – Conversion • Equipment • Planning • Installation of new equipment • File • Manual to electronic • Special programs to convert old format to new

SDLCImplementation – Conversion • System • Direct conversion • Phased conversion • Pilot conversion • Parallel conversion

SDLCImplementation –Auditing • Audit trail • Trace output back to source

SDLCImplementation – Evaluation • Working • Meets original requirements • Benefits • Meets budget • Improvements

SDLCImplementation – Maintenance Ongoing activity for life of system

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SDLC Phases. Phase 5: Systems Operation and SupportObjectivesProvide maintenance and improvements for the new information systemSupport users and help them obtain the most value from the new system. Chapter 12. Systems Operation and Support . Objectives. Explain how the systems operation and su

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Systems Analysis and Design. Romi Satria Wahon o [email protected] http://romisatriawahono.net 0 81586220090. Romi Satria Wahono. SD Sompok Semarang (1987) SMPN 8 Semarang (1990) SMA Taruna Nusantara , Magelang (1993)

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Systems Analysis and Design. SA&amp;D Outline. 9 Weeks of Lectures Supported by Tutorials Self Study Examples Bushmouth Lejk and Deeks. Expectations. Minimum Develop Accurate Data Flow Models Develop Accurate Data Models Apply Rules of Normalisation. Plan. Introduction Structured Methods

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section III Process design. for the Small Enterprise. Systems Analysis and Design. Software Categorization. 4GL Options Fourth Generation Languages Vertical Software - is custom designed for a specific segment of users, such as dentist offices, video stores, and so on

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Systems Analysis and Design. Entity Life Histories. (SSADM) Data Views. System Data. Function. Structure. DFD. LDS. Sequence. ELH. Sequence. Entity Name. Event C. Event A. Event B. Selection. Employee. Event D. New Employee. Event E. New Staff Recruited. New Trainee Accepted.

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Systems Analysis and Design. Rabie A. Ramadan Slides by Roberta M. Roth University of Northern Iowa. Questionnaires. A set of written questions, often sent to a large number of people May be paper-based or electronic Select participants using samples of the population

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Use our outstanding System Analysis and Design PowerPoint template to demonstrate how this process aims to assess the existing software systems/applications to identify the issues or requirements for the software development/redesigning purposes.

System analysts and designers can leverage these PowerPoint slides to illustrate the stages involved in the system development life cycle. The set can be used to depict the critical phases, need, steps, and importance of system analysis and design.

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• Co-ordinated by : IISc Bangalore
• Available from : 2009-12-31
• Data and Information
• Systems Analysis and Design Life Cycle
• Information gathering
• Feasibility analysis
• Data Flow Diagrams
• Process Specification
• Data input methods
• Logical Database Design
• Object oriented systems modeling
• Designing outputs
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Unpacking complexity in addressing the contribution of trauma to women’s ill health: a qualitative study of perspectives from general practice

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Background There is an intricate relationship between the mind and the body in experiences of health and wellbeing. This can result in complexity of both symptom presentation and experience. Although the contribution of life trauma to illness experience is well described, this is not always fully recognised or addressed in healthcare encounters. Negotiating effective and acceptable trauma-informed conversations can be difficult for clinicians and patients.

Aim To explore the experience of primary care practitioners caring for women through a trauma-informed care lens.

Design and setting Qualitative study in the general practice setting of England, with reflections from representatives of a group with lived experience of trauma.

Method This was a secondary thematic analysis of 46 qualitative interviews conducted online/by telephone to explore primary care practitioners’ experiences of supporting women’s health needs in general practice, alongside consultation with representatives of a lived-experience group to contextualise the findings.

Results Four themes were constructed: ‘you prioritise physical symptoms because you don’t want to miss something’; you do not want to alienate people by saying the wrong thing; the system needs to support trauma-informed care; and delivering trauma-informed care takes work that can have an impact on practitioners.

Conclusion Primary care practitioners are aware of the difficulties in discussing the interface between trauma and illness with patients, and request support and guidance in how to negotiate this supportively. Lack of support for practitioners moves the focus of trauma-informed care from a whole-systems approach towards individual clinician–patient interactions.

• biopsychosocial models
• communication
• general practice
• trauma-informed care
• Introduction

As evident in the Women’s Health Strategy for England 1 and its underlying public consultation, 2 women’s health is complex and embedded in historical dismissal and stigma. There is an intricate relationship between the mind and the body in experiences of health and wellbeing. One facet of this complexity includes the possible contribution of trauma to the woman’s illness experience. The physical response to, and pathways of bodily damage as a result of, the hormonal environment of chronic stress has revealed links between unresolved emotional distress and autoimmune conditions. 3 Trauma has an impact on people in different ways. Although some people make positive adjustments, others experience mental ill health and/or develop physical symptoms from emotional distress. 4 This can result in complexity both in symptom presentation and health experience.

Trauma can result from an event, series of events, or set of circumstances that is experienced by an individual as harmful or life threatening and can include past experiences of care (including in maternity), adverse childhood events (ACEs), and other life experiences as an adult. ACEs are stressful or traumatic events that occur specifically during childhood or adolescence 5 and can include: abuse (physical, emotional, and sexual); neglect; living in a household with domestic violence, experience of illness, or bereavement. 6 In a systematic review and meta-analysis of 96 studies of adult health behaviours, the risk of poorer health outcomes (including cardiovascular disease, respiratory disorders, gastrointestinal disorders, and mental ill health) increased with the number of ACEs. 4 Experiences of trauma at any stage in life can cause lasting adverse effects on health. 3 In the UK, women are disproportionally affected by violence (twice as likely as men to experience domestic violence), 7 , 8 trauma, 9 , 10 and ill health, 11 , 12 highlighting the potential complexity of women’s health presentation.

Although the contribution of life trauma to illness experience is well described, primary care professionals do not always fully address it. Potential reasons include clinician concerns about missing a serious illness in a complex presentation or about alienating or upsetting the patient. 11 Addressing trauma often necessitates introducing conversations about the link between mind and body, which can be difficult to navigate. Significant challenges and uncertainties reside in how best to manage the link between mind and body in communication with patients and in healthcare pathways. Qualitative research indicates that primary care professionals can find it challenging to navigate this mind–body presentation. Suggestions from primary care professionals that physical symptoms are amplified by (or a manifestation of) distress can be experienced as dismissal and invalidation by patients. 13 – 15 Attempts to bridge these health needs are therefore not always experienced as supportive. This illustrates the potential challenges of negotiating trauma-informed conversations in ways that are experienced as acceptable and supportive by patients.

How this fits in

Trauma-informed care is a framework founded on five core practices: safety, trustworthiness, choice, collaboration, and empowerment. These can be used to address the impact of trauma on patients and healthcare professionals and prevent re-traumatisation in healthcare services. 16 However, definitions, guidance, practitioner training, delivery, and support for trauma-informed approaches vary between healthcare settings according to local-level funding priorities with implementation described as disjointed. 16 Little is known about how healthcare professionals experience trying to effectively deliver trauma-informed care. The aim of this study was to explore the experiences of primary care practitioners caring for women through a trauma-informed care lens.

This study was a secondary analysis of qualitative interview data gathered to explore primary care practitioners’ experiences of supporting women’s health needs in primary care. Between March and September 2022, we interviewed a sample of 46 primary care practitioners across England (GPs n = 31, nurses n = 9, other professionals n = 6, with an average of 12 years’ experience [1 to 30 years], 41/46 female), ensuring representation from practices working in areas of deprivation where health inequalities and multimorbidity are significant challenges. Detailed methods and participant characteristics of the parent study are reported elsewhere. 17

The original topic guide was developed by three authors in response to a perceived gap in knowledge about women’s health care in primary care and commissioned by the National Institute of Health Research (NIHR) Policy Research Programme. Data were collected through single-episode, one-to-one interviews with fully informed consent. They were conducted virtually online or by telephone by two experienced qualitative researchers and audio-recorded. These were transcribed verbatim, checked against the original recording, and thematically analysed.

The team then undertook a focused enquiry using secondary thematic analysis of the dataset to explore primary care professionals’ navigation of women’s experiences of distress as a contribution to their symptoms. 18 We recoded the transcripts line-by-line where distress, emotional, or psychological impact or contribution to health experience was mentioned. We discussed the constructed data categories within the research team to create interpretive themes. We reflected on these themes with representatives of three charities supporting women with significant experience of historical and contemporary trauma to add a lived-experience perspective to the data.

Four themes were constructed from the data:

‘you prioritise physical symptoms because you don’t want to miss something’;

you do not want to alienate people by saying the wrong thing;

the system needs to support trauma-informed care; and

delivering trauma-informed care takes work that can have an impact on practitioners.

Theme 1: ‘you prioritise physical symptoms because you don’t want to miss something’ (PC30, female [F], GP for 5 years)

Practitioners described women’s health consultations as often complex and difficult to manage in a single, constrained time slot. A significant concern was the fear of missing a physical condition requiring specific or prompt treatment as many women’s health complaints could present with similar but vague symptomatology and could suggest multiple possible diagnoses. Some participants reflected that a challenge of navigating diagnostic processes, by first excluding potential causes that need specific interventions such as cancer, meant the contribution of distress to physical symptoms was pushed down the list of considerations: ‘It’s definitely sort of a symptom sieve to start with, and to adequately hear your patient and really hear them and really listen to what they’re saying […] There are many things that are difficult to do in ten minutes, but I … women’s health is particularly difficult.’ (PC17, F, advanced nurse practitioner [ANP] for more than 15 years) ‘They’re often quite vague symptoms: bloating, things like that, so you either have a very low index of suspicion and you’re seeing ca-125s [blood test that may indicate ovarian cancer] and you’re scanning everybody, or things get missed, and [sighs] yeah, it can be very challenging and obviously if you miss something like that it’s devastating for everybody involved, but it’s very difficult.’ (PC12, F, GP for more than 15 years)

Participants described how investigation pathways move through a hierarchy of potential causes and may involve a stepped process that did not always yield a confirmatory or unifying diagnosis. This meant that the participants had to manage patients’ expectations of diagnosis throughout this process.

Theme 2: you do not want to alienate people by saying the wrong thing

Some felt that a cultural shift was needed for the wider healthcare system to acknowledge the mind–body interplay as a legitimate expression of distress, to support practitioners to discuss this with their patients along their care pathway, and to provide timely access to psychological support services: ‘Perhaps some of training for staff would be about how you talk about the connection between your brain and your body […] without sounding dismissive and actually, training individuals to become more sensitive to these types of, conversations.’ (PC46, male [M], GP for 15 years)

However, some felt that patients were not always receptive to recognising the contribution of emotions or past experiences to physical symptoms, the idea of an integral link between mind and body, or the offer of psychological support to cope with the distress of physical symptoms. Some participants were worried about alienating women who might interpret this suggestion as devaluing or de-legitimising their symptom experience, and were therefore sometimes unsure when or how to navigate this: ‘I don’t think many patients like it when we end up going down that route when it comes to pain, any pain, not just pelvic pain in itself, because they want a diagnosis of some form or another, whatever it’s called, rather than being given some antidepressants or some counselling.’ (PC18, F, GP for 10 years)

Participants described the essential first step to be validation of the woman’s experience, emphasising understanding and genuine belief in the symptoms as ‘real’ (although perhaps currently unexplained) before exploring the impact of trauma or life stress in its aetiology: ‘It’s just spending the time with them and actually acknowledging, yes the pain is real, but are we not just saying you know, “you’ve got pain and we can’t find any cause for it”, “the pain is actually real”, and what we can do is maybe go down the route of psychological sort of therapy for that, that might be the best route of managing it.’ (PC18, F, GP for 10 years) ‘The first lady I was talking about absolutely wasn’t having any of it […] I got her some interesting resources […] and I just mis-pitched it […] the fact that this is her body feeling overwhelmed and feeling overwhelmed with the difficulties in her life and how to explain that in a way that seems scientific … it’s quite difficult, isn’t it?’ (PC14, F, GP for 1 year)

Healthcare professionals were aware and worried that exploring the contribution of trauma or distress in the physical symptom experience and that physical and emotional symptoms can coexist was not always well received. Restricted time in consultations highlighted the need for resources that could support this mind–body understanding in a positive and affirming way for the patient: ‘Often there is something organic, or something organic that has started it off, but then it often becomes this kind of complex combination of physical and then also psychological symptoms together, and I think kind of having resources to explain how psychological symptoms can impact pelvic pain […] I think kind of having good resources to try and back up what I’m saying would be quite helpful.’ (PC21, F, GP for more than 20 years)

Participants described how the net effect of these considerations could result in practitioners (inadvertently) avoiding discussion of the contribution of distress in the illness presentation: ‘ […] I think you can shut it down easily and not get emotionally involved, but you do not actually solve any of the issues unless they are straight up, simple, physical problems that you can just treat, but for the most part it doesn’t work very well.’ (PC30, F, GP for 5 years)

Participants recognised the importance of a trauma-informed approach in the complex and holistic care needs of women’s health. This extended to considerations about trauma-informed approaches to physical examination and how this could be enabled. Some highlighted the unique position of the primary care practitioner, in a potentially protracted diagnostic or support pathway, to communicate the contribution of distress in a supportive and helpful way to their patients.

Theme 3: the system needs to support trauma-informed care

Participants described four systemic challenges to the provision of trauma-informed care:

inadequate time allocated for appointments;

waiting times for specialist practitioner review in secondary care;

limited access to services; and

providing care for women returning from secondary care without a unifying diagnosis.

The challenges of time were frequently reported by participants: ‘I already know that I can’t do everything for you [the patient] in ten minutes, which isn’t always like a nice feeling for me, because we want to be able to help and you know do that within the time … who knows when they’ll be able to get an appointment again or you don’t want it to be frustrating for them, but equally you don’t want to rush yourself.’ (PC35, F, GP for less than 6 months) ‘They come back two months later and say, “I’ve still … I’m still … still haven’t seen the hospital”, and that there’s a certain amount of workload in primary care just because of … just because secondary care can’t take that on.’ (PC23, M, GP for more than 20 years)

In some areas they reported limited access to services such as counselling or psychological support services and community gynaecology because of local funding models and the challenges of providing care for women returning from secondary care without a unifying diagnosis. This often led to practitioners ‘holding the distress’ of the woman (see theme 4). Despite the challenges identified, participants described how they worked within the system constraints to offer the best service for their patients, for example, planning activities across multiple appointments: ‘In fifteen minutes it’s quite challenging, or if I’m trying to examine somebody […] that’s difficult, that’s when I sometimes ask them […] to come back for the examination so that I can do all the other things that are needed.’ (PC25, F, GP for 25 years)

Participants spoke of the structural supports that were in place that worked well in their efforts to deliver trauma-informed care, such as support networks, the ‘advice and guidance’ contact service to access secondary care (a system where GPs can access specialist advice before or instead of referral), and working with social prescribers (link workers who help patients to access non-medical support services in their community): ‘I mean advice and guidance [are] probably helpful I think, you write and you say, “What do I do?” and they tell you, and you then say to the patient, “this is what the specialist has said”, and that’s great, and that’s a really good idea.’ (PC23, M, GP for more than 20 years) ‘[Access to a social prescriber] is definitely making a difference; I don’t know what we did before to be quite honest. I don’t know what we would do because it’s just improved the quality of life for our patients, and it’s just helped us cope because you know we often see mental health problems, social problems, and with such a limited time constraint, limited resources, now that investment has been put in, it is definitely making a difference.’ (PC16, F, ANP for more than 18 years)

Theme 4: delivering trauma-informed care takes work and can have an impact on practitioners

Taking a trauma-informed approach relied heavily on the practitioner–patient relationship and some felt that the impact on practitioners was not always accounted for. The work involved in taking a trauma-informed approach to care had an impact on clinician workload. When they were able to navigate this challenge participants reported job satisfaction that was a positive impact. Conversely, when participants were unable to deliver the care they aspired to and believed they should, this had a negative impact. Protracted routes to diagnosis (or not getting a diagnosis), exacerbated by long waits to access specialist review in secondary care, left participants ‘holding the distress’ of women managing symptoms while they waited for a management plan: ‘I mean typically what happens is when a referral is done, the patient is waiting three, four, five months to be seen sometimes, but the patient’s still got those symptoms, so what do they do?’ (PC18, F, GP for 10 years) ‘So pain is complex. I think every pain service in the country is poorly funded and poorly accessible […] The challenge we have is these patients are constantly accessing us and, you know, I don’t want to label anything but they do end up becoming frequent attenders, which you know … and all we are is becoming a holding person in all of this.’ (PC46, M, GP for 15 years)

This increased the pressure on primary care practitioners who were operating without adequate system support. Although participants knew that managing uncertainty was integral to the role of the primary care practitioner, holding distress added to the challenge of appropriately broaching or exploring the mind–body link. Participants described feeling overwhelmed and personally affected by managing the expectations of patients held in limbo and holding their distress: ‘Women who have complex, like intractable symptoms that have been investigated and no one’s really come up with anything […] it’s more psycho-social input that’s needed, and they’ve seen a gynaecologist and they’re still struggling and there’s not really a solution, and so they’re … they’re the ones who you think, “oh my gosh, I … I’m … I’m not sure what I can offer … offer you”.’ (PC34, F, GP for 15 years) ‘I mean women’s health is a prime one, it causes so much anxiety, stress, impact on the family, and I think with the complexities around the referral pathways and who’s doing what, which has been one of my biggest stresses, people can fall through the gaps very easily.’ (PC26, F, GP for 5 years)

Participants sought support from colleagues within their daily work routines to reflect on clinical questions or patients with complex cases. However, some felt that there were limited support services for practitioners’ mental wellbeing in a more formalised and structured way: ‘We have our annual appraisal but that is very much to make sure that we’re not total lunatics […] but other than that […] they do support us, but they … you know it’s once a year, there’s no capacity to debrief on individual challenging cases or anything like that, it’s very much to check-in that we are sort of on the rails.’ (PC30, F, GP for 5 years)

Participants described how not being able to deliver high-quality, holistic care because of structural constraints was unsatisfying and challenging: ‘I was so unhappy in my previous job really, I’d say we still had support, but the patients were a lot more demanding and it just comes with that, you know a lot more child protection issues safeguarding and it … you know, it’s just a really challenging job and that, and not necessary work satisfying either.’ (PC04, F, GP for 3 years)

Lack of personal and systems support for practitioners moves the focus of trauma-informed care from a whole-systems approach to the clinician–patient interaction.

Our findings indicate that clinicians are aware of the contribution of trauma and distress to the presentation of physical symptomatology within women’s health consultations but that conversations about this could be difficult. Some participants felt confident and willing to discuss the role of distress in symptom presentation; others felt that these conversations were difficult and sometimes avoided the topic. Constraints such as limited time in consultations and the training and resources to facilitate discussions about the minded-body (the interconnection of physical and emotional health) and the role of trauma and distress could mean that clinicians did not always talk to patients about the impact of distress. This was exacerbated by system constraints such as limited support services for referral. Practitioners described building support mechanisms for themselves at work through debrief and clinical conversations with colleagues but told us that there were no formal supervision or support services routinely available for practitioners. The heavy work and emotional labour within an unsupportive system was described as contributing to practitioner frustration and burnout. Although patient relationships were framed within a trauma-informed lens, the organisational configuration was not always supportive to a trauma-informed approach.

Strengths and limitations

The use of secondary analysis has allowed us to conduct a focused analysis on a rich dataset of primary care professionals’ interviews. As this was done within the project timeline by the original research team, potential ethical concerns about the impact of the sociopolitical context that often accompanies secondary analysis were mitigated. 18 We were able to minimise participant burden and engage with a targeted group of women for whom trauma-informed care and its delivery has an immediate impact.

The principal limitation of our study is the restrictions offered by the original interview scope and guiding questions of the parent study that focused on women’s health. We are unable to report on experience in other areas of health care or by gender of care provider as this is unexplored. Gender was recorded; there were four male and 42 female responders. We purposively selected practitioners with an interest in women’s health rather than sampling an equally gender-split sample to derive patterns of experience that could be attributed to gender issues.

Comparison with existing literature

The link between trauma and ill health is well discussed in the literature, as are the principles of trauma-informed care. However, there appears to be little evidence of the clinician’s experience in discussing the interface between trauma and complexity with patients. The complexity of women’s health experiences challenges a dualistic approach to care and could respond better to the continuity model of primary care. 19 Practitioners in our data actively enacted the principles of trauma-informed care (such as safety, trustworthiness, and collaboration) in their personal practice with women. 16 However, the structural configuration of primary care services could complicate these care aspirations including when resources were limited or services were not flexible enough to support practitioner autonomy, which could hinder opportunities for timely care or follow-up. This could erode the practitioner’s efforts to deliver trauma-informed care, with potential consequences for both patients and clinicians. Such structural constraints in a climate of overwork are powerful sources of moral distress and burnout in studies of nurses, midwives, and doctors. 20 – 23 The risk of exposing practitioners to such moral distress can lead to the experience of vicarious trauma and reduced job satisfaction as they navigate the challenge of exploring the minded-body link with patients on their illness journey. 24 , 25 Primary care practitioners held women’s distress while they waited for specific therapies or supports, and yet the practitioners did not have adequate formal support systems to take care of their own wellbeing. This finding resonates with Pereira Gray et al , 25 who suggest that the UK shortage of GPs, erosion of continuity of care, sustained increase of remote consultation methods, and lack of structural support in the system may exacerbate challenges faced by practitioners to provide high-quality care. 26 – 28

Implications for research and practice

Our findings suggest that moving towards a trauma-informed systems-level approach would support integration of psychological support within multiple care pathways. A coordinated systems approach should support an integrated and holistic approach rather than encouraging a dichotomising split between physical or psychological services. Our findings suggest that this model would also support the wellbeing of practitioners delivering care and may have an impact on staff retention, making this a critical consideration at all system and service levels from individuals to practices to funders and commissioners. 28 , 29 However, less is known about how to enact or enable trauma-informed care at a systems level. 16 More research is needed about how to implement and support equitable, proportionate trauma-informed care in practice. This includes learning how to actively nurture equitable care within services, practices, and within primary care networks. At a funding and commissioning level, autonomy and equitable work need to be valued and enabled, and this requires policy attention; simplistic metrics of care such as numbers seen or a narrow focus on numerically quantifiable access will not capture either the impact on patients or practitioners. 28 Nor will this capture the contacts and appointments that did not happen. Furthermore, critical to effective equitable care is that practitioners need meaningful access to services that they can refer into and that will respond promptly and supportively to the needs identified. Work in areas of care such as female genital mutilation and domestic violence and abuse demonstrate that having acceptable accessible services to refer into enabled inquiry and compassionate care. 30 , 31 It is an ethical prerogative that trauma-informed enquiry is supported by trauma-informed services and support. Finally, support for staff is essential and the responsibility for this should not be devolved to individuals but commissioned and provided for. This contrasts with current policy, such as the wellbeing Quality and Outcomes Framework indicators that arguably devolve the responsibility for wellbeing to those in need of wellbeing support, without offering any tangible resources.

Healthcare professionals are aware of the difficulties in discussing the interface between trauma and complexity with patients 32 and our work shows they are requesting support and guidance in how to negotiate this supportively. The British Medical Association moral injury report 22 recommends systems changes that map onto the principles of trauma-informed care, including increased staffing, streamlining of bureaucracy, open and sharing work cultures, and provision of support for employees. However, although these recommendations acknowledge the problem and offer solutions, there is no requirement for organisations to address these structural concerns. Lack of these system supports for practitioners moves the focus of trauma-informed care from a whole-systems approach to the clinician–patient interaction. 16

To seek lived-experience perspectives on our findings, 33 we spoke with three representatives of charities supporting survival sex workers (SSW) in different regions of England as an exemplar vulnerable group with significant experience of historical and ongoing trauma. They told us how women experience stigma and are afraid of disclosure and confidentiality, particularly if their children have been removed and placed into social care. The charity representatives described how women engaged in SSW rarely sought medical care or achieved registration at a general practice surgery because of lifestyle circumstances and stigmatising experiences.

We asked what trauma-informed care looked like for their service and asked them to reflect on our findings. They recommended a systems-level approach to the delivery of trauma-informed services across the health service. Barriers to access were described as starting at the front door of the general practice surgery with the reaction of the receptionist. A lack of confidentiality in the reception area, closed consulting room doors, short consultation times, and the predominance of digital access methods for appointments were also cited. Beyond these, they suggested responsive, transparent pathways into support services for vulnerable women or those living in extreme circumstances would illustrate a trauma-informed approach to services. Individual practitioners were credited with adopting a trustworthy, trauma-informed approach but charity representatives, in consultation with the women they support, felt that the healthcare system could counteract individual good practice.

• Acknowledgments

We would like to acknowledge the contributions of our Public Involvement participants and express our thanks for the insights they shared with the research team.

This study was funded by the National Institute for Health and Care Research (NIHR) Policy Research Programme (NIHR202450). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Ethical approval

This study has received ethical approval from the Health Research Authority (ref 22/HRA/0985).

The authors do not have ethical permission to share their dataset beyond the study team.

Freely submitted; externally peer reviewed.

Competing interests

The authors have declared no competing interests.

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• Received January 12, 2024.
• Revision requested February 19, 2024.
• Accepted April 9, 2024.
• © The Authors

This article is Open Access: CC BY 4.0 licence ( http://creativecommons.org/licences/by/4.0/ ).

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