Each service we want to load balance can have a locally bound port e. This port is, actually, managed by HAProxy; every client request on this port will be received by the proxy and then passed to the backend service in an efficient way distributing load. Similarly, we can have proxies running between different server-side components.
HAProxy manages health checks and will remove or add servers to those pools. It also balances requests across all the servers in those pools. For most systems, we should start with a software load balancer and move to smart clients or hardware load balancing as the need arises.
Load balancing helps you scale horizontally across an ever-increasing number of servers, but caching will enable you to make vastly better use of the resources you already have, as well as making otherwise unattainable product requirements feasible.
Caches take advantage of the locality of reference principle: recently requested data is likely to be requested again. They are used in almost every layer of computing: hardware, operating systems, web browsers, web applications and more. A cache is like short-term memory: it has a limited amount of space, but is typically faster than the original data source and contains the most recently accessed items.
Caches can exist at all levels in architecture but are often found at the level nearest to the front end, where they are implemented to return data quickly without taxing downstream levels. Placing a cache directly on a request layer node enables the local storage of response data.
Each time a request is made to the service, the node will quickly return local, cached data if it exists. If it is not in the cache, the requesting node will query the data from disk. What happens when you expand this to many nodes? However, if your load balancer randomly distributes requests across the nodes, the same request will go to different nodes, thus increasing cache misses. Two choices for overcoming this hurdle are global caches and distributed caches.
In a distributed cache, each of its nodes own part of the cached data. Typically, the cache is divided up using a consistent hashing function, such that if a request node is looking for a certain piece of data, it can quickly know where to look within the distributed cache to determine if that data is available. In this case, each node has a small piece of the cache, and will then send a request to another node for the data before going to the origin.
Therefore, one of the advantages of a distributed cache is the ease by which we can increase the cache space, which can be achieved just by adding nodes to the request pool. A disadvantage of distributed caching is resolving a missing node. Some distributed caches get around this by storing multiple copies of the data on different nodes; however, you can imagine how this logic can get complicated quickly, especially when you add or remove nodes from the request layer.
A global cache is just as it sounds: all the nodes use the same single cache space. This involves adding a server, or file store of some sort, faster than your original store and accessible by all the request layer nodes. Each of the request nodes queries the cache in the same way it would a local one.
This kind of caching scheme can get a bit complicated because it is very easy to overwhelm a single cache as the number of clients and requests increase, but is very effective in some architectures particularly ones with specialized hardware that make this global cache very fast, or that have a fixed dataset that needs to be cached.
There are two common forms of global caches depicted in the following diagram. First, when a cached response is not found in the cache, the cache itself becomes responsible for retrieving the missing piece of data from the underlying store.
Second, it is the responsibility of request nodes to retrieve any data that is not found in the cache. Most applications leveraging global caches tend to use the first type, where the cache itself manages eviction and fetching data to prevent a flood of requests for the same data from the clients. However, there are some cases where the second implementation makes more sense. For example, if the cache is being used for very large files, a low cache hit percentage would cause the cache buffer to become overwhelmed with cache misses; in this situation, it helps to have a large percentage of the total data set or hot data set in the cache.
This could be because of application requirements around that data latency—certain pieces of data might need to be very fast for large data sets—where the application logic understands the eviction strategy or hot spots better than the cache. CDNs are a kind of cache that comes into play for sites serving large amounts of static media.
While caching is fantastic, it does require some maintenance for keeping cache coherent with the source of truth e. If the data is modified in the database, it should be invalidated in the cache, if not, this can cause inconsistent application behavior. Write-through cache: Under this scheme data is written into the cache and the corresponding database at the same time. The cached data allows for fast retrieval, and since the same data gets written in the permanent storage, we will have complete data consistency between cache and storage.
Also, this scheme ensures that nothing will get lost in case of a crash, power failure, or other system disruptions. Although write through minimizes the risk of data loss, since every write operation must be done twice before returning success to the client, this scheme has the disadvantage of higher latency for write operations.
Write-around cache: This technique is similar to write through cache, but data is written directly to permanent storage, bypassing the cache. Write-back cache: Under this scheme, data is written to cache alone, and completion is immediately confirmed to the client.
The write to the permanent storage is done after specified intervals or under certain conditions. This results in low latency and high throughput for write-intensive applications, however, this speed comes with the risk of data loss in case of a crash or other adverse event because the only copy of the written data is in the cache. Data partitioning also known as sharding is a technique to break up a big database DB into many smaller parts.
The justification for data sharding is that, after a certain scale point, it is cheaper and more feasible to scale horizontally by adding more machines than to grow it vertically by adding beefier servers. There are many different schemes one could use to decide how to break up an application database into multiple smaller DBs. Below are three of the most popular schemes used by various large scale applications.
Horizontal partitioning: In this scheme, we put different rows into different tables. For example, if we are storing different places in a table, we can decide that locations with ZIP codes less than are stored in one table, and places with ZIP codes greater than are stored in a separate table.
This is also called a range based sharding, as we are storing different ranges of data in separate tables. In the previous example, splitting location based on their zip codes assumes that places will be evenly distributed across the different zip codes. This assumption is not valid as there will be a lot of places in a thickly populated area like Manhattan compared to its suburb cities.
Vertical Partitioning: In this scheme, we divide our data to store tables related to a specific feature to their own server. For example, if we are building Instagram like application, where we need to store data related to users, all the photos they upload and people they follow, we can decide to place user profile information on one DB server, friend lists on another and photos on a third server. Vertical partitioning is straightforward to implement and has a low impact on the application.
The main problem with this approach is that if our application experiences additional growth, then it may be necessary to further partition a feature specific DB across various servers e. Directory Based Partitioning: A loosely coupled approach to work around issues mentioned in above schemes is to create a lookup service which knows your current partitioning scheme and abstracts it away from the DB access code.
So, to find out where does a particular data entity resides, we query our directory server that holds the mapping between each tuple key to its DB server.
This loosely coupled approach means we can perform tasks like adding servers to the DB pool or change our partitioning scheme without having to impact your application. Key or Hash-based partitioning: Under this scheme, we apply a hash function to some key attribute of the entity we are storing, that yields the partition number.
For example, if we have DB servers and our ID is a numeric value that gets incremented by one, each time a new record is inserted. This approach should ensure a uniform allocation of data among servers. The fundamental problem with this approach is that it effectively fixes the total number of DB servers, since adding new servers means changing the hash function which would require redistribution of data and downtime for the service.
A workaround for this problem is to use Consistent Hashing. List partitioning: In this scheme, each partition is assigned a list of values, so whenever we want to insert a new record, we will see which partition contains our key and then store it there. For example, we can decide all users living in Iceland, Norway, Sweden, Finland or Denmark will be stored in a partition for the Nordic countries.
Round-robin partitioning: This is a very simple strategy that ensures uniform data distribution. Composite partitioning: Under this scheme, we combine any of above partitioning schemes to devise a new scheme.
For example, first applying a list partitioning and then a hash based partitioning. Consistent hashing could be considered a composite of hash and list partitioning where the hash reduces the key space to a size that can be listed. On a sharded database, there are certain extra constraints on the different operations that can be performed.
Most of these constraints are due to the fact that, operations across multiple tables or multiple rows in the same table, will no longer run on the same server. Below are some of the constraints and additional complexities introduced by sharding:. Joins and Denormalization: Performing joins on a database which is running on one server is straightforward, but once a database is partitioned and spread across multiple machines it is often not feasible to perform joins that span database shards.
Such joins will not be performance efficient since data has to be compiled from multiple servers. A common workaround for this problem is to denormalize the database so that queries that previously required joins can be performed from a single table.
Of course, the service now has to deal with all the perils of denormalization such as data inconsistency. Referential integrity: As we saw that performing a cross-shard query on a partitioned database is not feasible, similarly trying to enforce data integrity constraints such as foreign keys in a sharded database can be extremely difficult.
Most of RDBMS do not support foreign keys constraints across databases on different database servers. Which means that applications that require referential integrity on sharded databases often have to enforce it in application code. Often in such cases, applications have to run regular SQL jobs to clean up dangling references.
Rebalancing: There could be many reasons we have to change our sharding scheme:. In such cases, either we have to create more DB shards or have to rebalance existing shards, which means the partitioning scheme changed and all existing data moved to new locations. Doing this without incurring downtime is extremely difficult. Using a scheme like directory based partitioning does make rebalancing a more palatable experience at the cost of increasing the complexity of the system and creating a new single point of failure i.
Indexes are well known when it comes to databases. Sooner or later there comes a time when database performance is no longer satisfactory. One of the very first things you should turn to when that happens is database indexing. The goal of creating an index on a particular table in a database is to make it faster to search through the table and find the row or rows that we 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 records. A library catalog is a register that contains the list of books found in a library.
The catalog is organized like a database table generally with four columns: book title, writer, subject and date of publication. There are usually two such catalogs, one sorted by the book title and one sorted by the writer name. We will also teach you some strategies for presenting your knowledge and skills in the best possible way. The course is still being developed and more example system design questions will be added in the near future. Building applications is not a rocket science, but having the vision of the overall architecture really makes a difference.
We have to crack the system interview sooner or later in our career as a software engineer or engineering manager. Interviewers are looking for future teammates that they like to work with. The future teammates are expected to be, at least, capable of solving problems independently.
There are so many solutions to any given problem, but not all of them are suited given the context. So the interviewee has to specify different choices and their tradeoffs. Keep talking for 45 mins could be easy, as long as we are armed with the following four steps and three common topics. Breaking down a complex task into small chunks helps us handle the problem at a better pace and in a more actionable way.
Do not dive into details before outlining the big picture. Otherwise, going off too far towards a wrong direction would make it harder to even provide a roughly correct solution. We will regret wasting time on irrelevant details when we do not have time to finish the task. OK, let us sketch out the following diagram without concerning too much about the implementation detail of these components. When we truly understand a system, we should be able to identify what each component is and explain how they interact with one another.
Take these components in the above diagram and specify each one by one. This could lead to more general discussions, such as the three common topics in Section 2, and to more specific domains, like how to design the photo storage data layout….
It is good enough to talk in this level of detail on this topic, but in case the interviewer wants more, we can suggest exact algorithms like round robin, weighted round robin, least loaded, least loaded with slow start, utilization limit, latency, cascade, etc. Reverse proxy, like varnish, centralizes internal services and provides unified interfaces to the public.
For example, www. Reverse proxy could also help with caching and load balancing. There are two major bottlenecks of the whole system — requests per second rps and bandwidth. We could improve the situation by using more efficient tech stack, like frameworks with async and non-blocking reactor pattern, and enhancing the hardware, like scaling up aka vertical scaling or scaling out aka horizontal scaling. Internet companies prefer scaling out, since it is more cost-efficient with a huge number of commodity machines.
This is also good for recruiting, because the target skillsets are equipped by. After all, people rarely play with super computers or mainframes at home. Frontend web tier and service tier must be stateless in order to add or remove hosts conveniently, thus achieving horizontal scalability.
Traditionally, view or template is rendered to HTML by the server at runtime. In the age of mobile computing, view can be as simple as serving the minimal package of data transporting to the mobile devices, which is called web API. People believe that the API can be shared by clients and browsers. And that is why single page web applications are becoming more and more popular, especially with the assistance of frontend frameworks like react.
The single responsibility principle advocates small and autonomous services that work together, so that each service can do one thing well and not block others. Grokking the system design github. Top 6 resources to look at before your tech interview shecancode. Advanced system design educative download, advanced system design educative pdf. I was looking for good book resources after several people have been asking me how they can get better at building distributed systems or learning designing systems at scale.
They 're used to gather information about the pages you visit and how clicks. Grokking the System Design Interview.
Master Scalability and System Design. How You'll Learn Hands-on coding environments. Faster than videos. No set-up required. Progress you can show. Hands-on coding environments. Course Contents Hide All. Hide All Lessons. System Design Problems.
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