Grokking the system design interview free download

Key-Value Stores: Data is stored in an array of key-value pairs. Well-known key value stores include Redis, Voldemort and Dynamo. Document Databases: In these databases data is stored in documents, instead of rows and columns in a table, and these documents are grouped together in collections. Each document can have an entirely different structure.

Columnar databases are best suited for analyzing large datasets - big names include Cassandra and HBase. Graph Databases: These databases are used to store data whose relations are best represented in a graph.

Data is saved in graph structures with nodes entities , properties information about the entities and lines connections between the entities. Examples of graph database include Neo4J and InfiniteGraph.

NoSQL databases have different data storage models. The main ones are key-value, document, graph and columnar. We will discuss differences between these databases below. Schema: In SQL, each record conforms to a fixed schema, meaning the columns must be decided and chosen before data entry and each row must have data for each column. The schema can be altered later, but it involves modifying the whole database and going offline.

Whereas in NoSQL, schemas are dynamic. Querying: SQL databases uses SQL structured query language for defining and manipulating the data, which is very powerful. In NoSQL database, queries are focused on a collection of documents.

Different databases have different syntax for using UnQL. Scalability: In most common situations, SQL databases are vertically scalable, i. It is possible to scale a relational database across multiple servers, but this is a challenging and time-consuming process. On the other hand, NoSQL databases are horizontally scalable, meaning we can add more servers easily in our NoSQL database infrastructure to handle large traffic.

Any cheap commodity hardware or cloud instances can host NoSQL databases, thus making it a lot more cost-effective than vertical scaling. A lot of NoSQL technologies also distribute data across servers automatically. So, when it comes to data reliability and safe guarantee of performing transactions, SQL databases are still the better bet. Even as NoSQL databases are gaining popularity for their speed and scalability, there are still situations where a highly structured SQL database may perform better; choosing the right technology hinges on the use case.

When all the other components of our application are fast and seamless, NoSQL databases prevent data from being the bottleneck. Big data is contributing to a large success for NoSQL databases, mainly because it handles data differently than the traditional relational databases.

CAP theorem states that it is impossible for a distributed software system to simultaneously provide more than two out of three of the following guarantees CAP : Consistency, Availability and Partition tolerance. When we design a distributed system, trading off among CAP is almost the first thing we want to consider.

CAP theorem says while designing a distributed system we can pick only two of:. Consistency: All nodes see the same data at the same time. Consistency is achieved by updating several nodes before allowing further reads.

Availability is achieved by replicating the data across different servers. Partition tolerance: System continues to work despite message loss or partial failure. Data is sufficiently replicated across combinations of nodes and networks to keep the system up through intermittent outages. We cannot build a general data store that is continually available, sequentially consistent and tolerant to any partition failures.

We can only build a system that has any two of these three properties. Because, to be consistent, all nodes should see the same set of updates in the same order. But if the network suffers a partition, updates in one partition might not make it to the other partitions before a client reads from the out-of-date partition after having read from the up-to-date one.

Hash Tables need key, value and a hash function, where hash function maps the key to a location where the value is stored. Suppose we are designing a distributed caching system. It is simple and commonly used. But it has two major drawbacks:. Consistent hashing is a very useful strategy for distributed caching system and DHTs.

It allows distributing data across a cluster in such a way that will minimize reorganization when nodes are added or removed. Hence, making the caching system easier to scale up or scale down. In Consistent Hashing when the hash table is resized e. In consistent hashing objects are mapped to the same host if possible. As a typical hash function, consistent hashing maps a key to an integer. Suppose the output of the hash function is in the range of [0, Imagine that the integers in the range are placed on a ring such that the values are wrapped around.

To add a new server, say D, keys that were originally residing at C will be split. Some of them will be shifted to D, while other keys will not be touched. To remove a cache or if a cache failed, say A, all keys that were originally mapping to A will fall into B, and only those keys need to be moved to B, other keys will not be affected. For load balancing, as we discussed in the beginning, the real data is essentially randomly distributed and thus may not be uniform.

It may make the keys on caches unbalanced. Instead of mapping each cache to a single point on the ring, we map it to multiple points on the ring, i. This way, each cache is associated with multiple portions of the ring. Long-Polling, WebSockets, and Server-Sent Events are popular communication protocols between a client like a web browser and a web server. Following are a sequence of events for regular HTTP request:.

Polling is a standard technique used by the vast majority of AJAX applications. The basic idea is that the client repeatedly polls or requests a server for data. The client makes a request and waits for the server to respond with data. If no data is available, an empty response is returned. Problem with Polling is that the client has to keep asking the server for any new data.

As a result, a lot of responses are empty creating HTTP overhead. A variation of the traditional polling technique that allows the server to push information to a client, whenever the data is available. With Long-Polling, the client requests information from the server exactly as in normal polling, but with the expectation that the server may not respond immediately.

It provides a persistent connection between a client and a server that both parties can use to start sending data at any time. The client establishes a WebSocket connection through a process known as the WebSocket handshake. If the process succeeds, then the server and client can exchange data in both directions at any time.

The WebSocket protocol enables communication between a client and a server with lower overheads, facilitating real-time data transfer from and to the server. This is made possible by providing a standardized way for the server to send content to the browser without being asked by the client, and allowing for messages to be passed back and forth while keeping the connection open.

In this way, a two-way bi-directional ongoing conversation can take place between a client and a server. Under SSEs the client establishes a persistent and long-term connection with the server. The server uses this connection to send data to a client. SSEs are best when we need real-time traffic from the server to the client or if the server is generating data in a loop and will be sending multiple events to the client.

Key characteristics of a distributed systems include Scalability, Reliability, Availability, Efficiency, and Manageability. Scalability is the capability of a system, process or a network to grow and manage increased demand.

Any distributed system that can continuously evolve in order to support the growing amount of work is considered to be scalable.

A system may have to scale because of many reasons like increased data volume or increased amount of work, e. A scalable system would like to achieve this scaling without performance loss. Generally, the performance of a system, although designed or claimed to be scalable, declines with the system size, due to the management or environment cost.

For instance, network speed may become slower because machines tend to be far apart from one another. More generally, some tasks may not be distributed, either because of their inherent atomic nature or because of some flaw in the system design. At some point, such tasks would limit the speed-up obtained by distribution. A scalable architecture avoids this situation and attempts to balance the load on all the participating nodes evenly.

Horizontal vs. Vertical Scaling: Horizontal scaling means that you scale by adding more servers into your pool of resources whereas Vertical scaling means that you scale by adding more power CPU, RAM, Storage, etc. With horizontal-scaling it is often easier to scale dynamically by adding more machines into the existing pool; Vertical-scaling is usually limited to the capacity of a single server, scaling beyond that capacity often involves downtime and comes with an upper limit.

Good examples of horizontal scaling are Cassandra and MongoDB , as they both provide an easy way to scale horizontally by adding more machines to meet growing needs. Similarly a good example of vertical scaling is MySQL as it allows for an easy way to scale vertically by switching from small to bigger machines.

However, this process often involves downtime. By definition, reliability is the probability a system will fail in a given period. In simple terms, a distributed system is considered reliable if it keeps delivering its services even when one or several of its software or hardware components fail.

Reliability represents one of the main characteristics of any distributed system, as in such systems any failing machine can always be replaced by another healthy one, ensuring the completion of the requested task. Take the example of a large electronic commerce store like Amazon , where one of the primary requirement is that any user transaction should never be canceled due to a failure of the machine that is running that transaction. For instance, if a user has added an item to their shopping cart, the system is expected not to lose it.

A reliable distributed system achieves this through redundancy of both the software components and data. Obviously, redundancy has a cost, and a reliable system has to pay that to achieve such resilience for services by eliminating every single point of failure. By definition, availability is the time a system remains operational to perform its required function, in a specific period. It is a simple measure of the percentage of time that a system, service, or a machine remains operational under normal conditions.

An aircraft that can be flown for many hours a month without much downtime can be said to have a high availability. Availability takes into account maintainability, repair time, spares availability, and other logistics considerations. If an aircraft is down for maintenance, it is considered not available during that time. Reliability is availability over time considering the full range of possible real-world conditions that can occur.

An aircraft that can make it through any possible weather safely is more reliable than one that has vulnerabilities to possible conditions. Reliability Vs. Availability If a system is reliable, it is available. However, if it is available, it is not necessarily reliable. In other words, high reliability contributes to high availability, but it is possible to achieve a high availability even with an unreliable product by minimizing repair time and ensuring that spares are always available when they are needed.

However, the system was launched without any information security testing. In the third year, the system experiences a series of information security incidents that suddenly result in extremely low availability for extended periods of time. This results in reputational and financial damage to the customers.

Two standard measures of its efficiency are the response time or latency that denotes the delay to obtain the first item, and the throughput or bandwidth which denotes the number of items delivered in a given time unit e. The two measures correspond to the following unit costs:.

The complexity of operations supported by distributed data structures e. It ignores the impact of many aspects, including the network topology, the network load and its variation, the possible heterogeneity of the software and hardware components involved in data processing and routing, etc.

Another important consideration while designing a distributed system is how easy it is to operate and maintain. Serviceability or manageability is the simplicity and speed with which a system can be repaired or maintained; if the time to fix a failed system increases, then availability will decrease.

Things to consider for manageability are the ease of diagnosing and understanding problems when they occur, ease of making updates or modifications, and how simple the system is to operate i. Early detection of faults can decrease or avoid system downtime. For example, some enterprise systems can automatically call a service center without human intervention when the system experiences a system fault.

In system design interviews, candidates are required to show their ability to develop a high-level architecture of a large system. Designing software systems is a very broad topic and even a software engineer having years of experience at a top software company may not claim to be an expert on system design. In real life, companies spend not weeks but months and hire a big team of software engineers to build such systems.

Given this, then how can a person answer such a question in 40 minutes? Moreover, there is no set pattern of such questions. Finally, a book for getting better at architecting systems. Grokking the object oriented design interview pdf github.

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It can also be an indicator of how good you are. Grokking the system design interview course. System Design Problems. Glossary of System Design Basics.

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