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Podstawy projektowania systemówNauka projektowania systemów dużej skali.Nauka projektowania systemów dużej skali.
Przygotowanie do rozmowy o pracę na stanowisko projektanta systemów.
Nauka projektowania skalowalnych systemów pozwoli ci stać się lepszym inżynierem.
Projektowanie systemów jest obszernym tematem. W Internecie istnieje duża ilość zasobów opisujących zasady ich projektowania.
To repozytorium jest zorganizowanym zbiorem materiałów, które powinny pomóc w nauce budowy dużych systemów.
Nauka od środowiska open sourceJest to ciągle aktualizowany projekt open source.
Zachęcamy do kontrbucji!
Przygotowanie do rozmowy o pracęPoza zwyczajowym testem z kodowania, projektowanie systemów jest niezbędnym etapem rozmowy o pracę, w wielu firmach.
Ćwicz typowe zadania z projektowania systemów i porównuj swoje odpowiedzi z przykładowymi rozwiązaniami: dyskusjami, kodem i diagramami.
Dodatkowe tematy związane z przygotowaniem do rozmowy o pracę:
Możesz powtarzać materiał i zapamiętywać główne idee, używane podczas projektowania systemów, korzystając z dostępnych Fiszkach Anki.
Świetnie sprawdzają się w podróży!
Materiały z kodowania: Interactive Coding ChallengesPoszukujesz materiałów, które pomogą w przygotowaniach do części rozmowy o pracę związanej z Kodowaniem?
Sprawdź powiązane repo Interactive Coding Challenges, które zawiera dodatkowy zestaw Anki:
Ucz się od społeczności.
Zachęcamy do pomocy (przez pull requesty) przy:
Materiały, które wymagają dopracowania są w rozdziale Praca w toku.
Koniecznie przeczytaj Contributing Guidelines!
Spis tematów związanych z projektowaniem systemówPodsumowanie różnych tematów dotyczących projektowania systemów, wraz z wadami i zaletami. Wszystko jest kompromisem.
Każdy rozdział zawiera linki do materiałów uszczegóławiających zagadnienie.
Proponowana lista tematów, do odświeżenia, w zalężności od dlugości rozmowy o pracę (krótkie, średnie, długie).
Q: Czy muszę wiedzieć wszystko na temat podczas rozmowy o pracę?
A: Nie, nikt nie będzie oczekiwał, że opanujesz cały materiał podczas przygotywań.
O co zostaniesz zapytany zależy od:
Zasadniczo, od kandydatów z większym doświadczniem oczekuje się większej wiedzy z zakresu projetkowania systemów. Architekci i liderzy zespołów powinni wiedzieć więcej niż "zwykli deweloperzy". Podczas rozmowy o pracę do największych firm z branży można oczekiwać conajmniej jednej serii pytań o projektowanie systemów.
Zacznij od wiedzy ogólnej, kolejno zagłębiaj się w kilka wybranych obszarów. Jeśli wiesz więcej o projektowaniu kluczowych elementów systemu, tym lepiej. Wybierz ścieżkę z poradnika w zależności od tego ile masz czasu, jakie masz doświadczenie, na jaką pozycję aplikujesz i wreszcie, w jakiej firmie starasz się o pracę.
Szybka ścieżka - Celem jest zdobycie szerokiej wiedzy o projektowaniu systemów. W ramach praktyki rozwiąż kilka zadań z rozmów o pracę.
Średnia ścieżka - Celem jest zdobycie szerokiej wiedzy o projektowaniu systemów i pogłębienie jej w kilku wybranych zagadnieniach. W ramach praktyki rozwiąż wiele zadań z rozmów o pracę.
Długa ścieżka - Celem jest zdobycie szerokiej wiedzy o projektowaniu systemów i dalsze jej pogłębienie. W ramach praktyki rozwiąż większość zadań z rozmów o pracę.
Jak odpowiadać na pytania o projektowaniu systemów
Rozmowa kwalifikacyjnej z projektowania systemów przynosi ze sobą pytania otwarte i oczekuje się, że rekrutowany będzie stroną prowadzącą.
Podczas rozmowy można posłużyć się poniższym wzorem, żeby skierować ją na właściwe tory. Aby umocnić swoje rozumienie tematu, należy przejrzeć dział Pytania na rozmowie kwalifikacyjnej z rozwiązaniami i skorzystać z poniższych kroków.
Krok 1: Zarysowanie przypadku użycia, ograniczeń i założeńCelem kroku jest zebranie wymagań i określanie problemu. Podczas tego kroku należy zadawać pytania aby wyjaśnić przypadek użycia i ograniczenia. Należy także przedstawić założenia jakie ma się na rozwiązanie problemu.
Stwórz wysokopoziomowy projekt systemu zarysowując najważniejsze jego komponenty.
Dla każdego kluczowego elementu stwórz dokładniejszy projekt. Na przykład, jeśli zadaniem jest stworzenie projektu serwisu skracającego URL, do omówienia są następujące zagadnienia:
Celem kroku jest zidentyfikowanie potencjalnych wąskich gardeł i podanie ograniczeń. Na przykład, czy w rozwiązaniu problemów ze skalowalnością pomogą:
Należy umówić potencjalne rozwiązania i kompromisy. Wszystko jest pewnego rodzaju kompromisem. Trzeba zaadresować problemy z przepustowością używając zasad projektowania skalowalnych systemów.
Podczas rozmowy można zostać poproszonym o odręczne wykonanie estymat. W Dodatek znajdują się następujące zasoby, które mogą być przydatne w tym zadaniu:
Poniżej znajduje się lista dodatkowych materiałów, które pozwolą zrozumieć jakie są oczekiwania:
Pytania Zaprojektuj Pastebin.com (lub Bit.ly) Solution Zaprojektuj timeline oraz wyszukiwarkę Twittera (lub Facebooka) Solution Zaprojektuj web crawler'a Solution Zaprojektuj Mint.com Solution Zaprojektur struktury danych dla strony społecznościowej Solution Zaprojektuj strukturę klucz-wartość dla silnika wyszukiwarki Solution Zaprojektuj Amazonowy ranking sprzedaży z filtrowaniem po kategoriach Solution Zaprojektuj system, który skaluje się na miliony użytkowników przy użyciu AWS Solution Dodaj pytanie z projektowania systemów Contribute Zaprojektuj Pastebin.com (lub Bit.ly) Zaprojektuj timeline i wyszukiwarkę Twittera (lub Facebooka) Zaprojektur struktury danych dla social network Zaprojektuj strukturę klucz-wartość dla silnika wyszukiwarki Zaprojektuj Amazonowy ranking sprzedaży z filtrowaniem po kategoriach Zaprojektuj system, który skaluje się na milionów użytkowników przy użyciu AW Object-oriented design interview questions with solutionsCzęste pytania z proejtkowania systemów razem z omówieniem rozwiązań, kodem i diagramami.
Rozwiązania linkują do katalogu
solutions/.
Common object-oriented design interview questions with sample discussions, code, and diagrams.
Solutions linked to content in the
solutions/folder.
Projektowanie systemów: zacznij tutajNote: This section is under development
Jesteś nowy w projektowaniu systemów?
Po pierwsze, będziesz potrzebował podstawowego zrozumienia ogólnych zasad, poznania, czym są, w jaki sposób są one stosowane, oraz ich zalet i wad.
Krok 1: Zapoznaj się z wykładem wideo na temat skalowalnościScalability Lecture at Harvard
Następnie przyjrzymy się kompromisom na "wysokim poziomie" (high-level):
Pamiętaj o tym, że wszystko jest kompromisem.
Następnie zajmiemy się bardziej szczegółowymi tematami, takimi jak: DNS, CDN i load balancery.
Wydajność kontra skalowalnośćUsługa jest skalowalna, jeśli skutkuje zwiększoną wydajnością w sposób proporcjonalny do dodanych zasobów. Ogólnie rzecz biorąc, zwiększenie wydajności oznacza obsługę większej ilości jednostek pracy, ale może to być również obsługa większych jednostek pracy, np. gdy rosną zestawy danych.1
Inny sposób patrzenia na wydajność kontra skalowalność:
Opóźnienie to czas wykonania jakiejś operacji lub uzyskania jakiegoś rezultatu.
Przepustowość to liczba takich operacji lub wyników na jednostkę czasu.
Ogólnie rzecz biorąc, należy dążyć do maksymalnej przepustowości przy akceptowalnym opóźnieniu.
Dostępność kontra spójnośćW rozproszonym systemie komputerowym można obsługiwać tylko dwie z poniższych gwarancji:
Sieci nie są niezawodne, dlatego trzeba będzie wspierać tolerancję podziału. Będziesz musiał dokonać kompromisu pomiędzy spójnością i dostępnością oprogramowania.
CP - spójność i tolerancja podziałuOczekiwanie na odpowiedź od partycjonowanego węzła może powodować błąd przekroczenia maksymalnego czasu oczekiwania. CP jest dobrym wyborem jeśli jednym z założeń biznesowych jest atomowy odczyt i zapis.
AP - dostępność i tolerancja podziałuResponderzy zwracają najaktualniejszą wersję danch dostępnych w węźle, nie znaczy to, że jest to ostatnia wersja. Zapisy mogą trwać, z powodu propagacji danych związanej z ich partycjonowaniem.
AP jest dobrym wyborem jeśli biznes powinien pozwalać na eventual consistency lub gdy system musi działać pomimo zewnętrznych błędów.
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.
Source(s) and further readingThere are two main 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): failoverThis topic is further discussed in the Database section:
System nazw domen (Domain Name System)
Source: DNS security presentation
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).
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:
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:
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.
Source: Scalable system design patterns
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:
Load balancers can be implemented with hardware (expensive) or with software such as HAProxy.
Additional benefits include:
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:
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 terminates 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 scalingA 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.
Additional benefits include:
Source: Intro to architecting systems for scale
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.
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
Source: Scaling up to your first 10 million users
A relational database like SQL is a collection of data items organized in tables.
ACID is a set of properties of relational database transactions.
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.
Source: Scalability, availability, stability, patterns
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.
Source: Scalability, availability, stability, patterns
Source: Scaling up to your first 10 million users
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
Source: Scalability, availability, stability, patterns
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): shardingDenormalization 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): denormalizationSQL 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.
Benchmarking and profiling might point you to the following optimizations.
CHAR instead of VARCHAR for fixed-length fields.
CHAR effectively allows for fast, random access, whereas with VARCHAR, you must find the end of a string before moving onto the next one.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.INT for larger numbers up to 2^32 or 4 billion.DECIMAL for currency to avoid floating point representation errors.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.NOT NULL constraint where applicable to improve search performance.SELECT, GROUP BY, ORDER BY, JOIN) could be faster with indices.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.
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 storeAbstraction: 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 in either 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
Source: SQL & NoSQL, a brief history
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 storeAbstraction: 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
Source: Transitioning from RDBMS to NoSQL
Reasons for SQL:
Reasons for NoSQL:
Sample data well-suited for NoSQL:
Source: Scalable system design patterns
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:
There are multiple levels you can cache that fall into two general categories: database queries and objects:
Generally, you should try to avoid file-based caching, as it makes cloning and auto-scaling more difficult.
Caching at the database query levelWhenever you query the database, hash the query as a key and store the result to the cache. This approach suffers from expiration issues:
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):
Suggestions of what to 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.
Source: From cache to in-memory data grid
The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following:
def get_user(self, user_id):
user = cache.get("user.{0}", user_id)
if user is None:
user = db.query("SELECT * FROM users WHERE user_id = {0}", user_id)
if user is not None:
key = "user.{0}".format(user_id)
cache.set(key, json.dumps(user))
return user
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
Source: Scalability, availability, stability, patterns
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 code:
set_user(12345, {"foo":"bar"})
Cache code:
def set_user(user_id, values):
user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
cache.set(user_id, user)
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
Source: Scalability, availability, stability, patterns
In write-behind, the application does the following:
Source: From cache to in-memory data grid
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
Source: Intro to architecting systems for scale
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:
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): asynchronismHTTP 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:
Verb Description Idempotent* Safe Cacheable GET Reads a resource Yes Yes Yes POST Creates a resource or trigger a process that handles data No No Yes if response contains freshness info PUT Creates or replace a resource Yes No No PATCH Partially updates a resource No No Yes if response contains freshness info DELETE Deletes a resource Yes No No*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 Transmission control protocol (TCP)
Source: How to make a multiplayer game
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:
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:
Source: How to make a multiplayer game
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:
Source: Crack the system design interview
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:
Sample RPC calls:
GET /someoperation?data=anId
POST /anotheroperation
{
"data":"anId";
"anotherdata": "another value"
}
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:
HTTP APIs following REST tend to be used more often for public APIs.
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:
Sample REST calls:
GET /someresources/anId
PUT /someresources/anId
{"anotherdata": "another value"}
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.
Source: Do you really know why you prefer REST over RPC
Source(s) and further reading: REST and RPCThis 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:
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.
Power Exact Value Approx Value Bytes
---------------------------------------------------------------
7 128
8 256
10 1024 1 thousand 1 KB
16 65,536 64 KB
20 1,048,576 1 million 1 MB
30 1,073,741,824 1 billion 1 GB
32 4,294,967,296 4 GB
40 1,099,511,627,776 1 trillion 1 TB
Latency Comparison Numbers
--------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 100 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 10,000 ns 10 us
Send 1 KB bytes over 1 Gbps network 10,000 ns 10 us
Read 4 KB randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
Read 1 MB sequentially from memory 250,000 ns 250 us
Round trip within same datacenter 500,000 ns 500 us
Read 1 MB sequentially from SSD* 1,000,000 ns 1,000 us 1 ms ~1GB/sec SSD, 4X memory
Disk seek 10,000,000 ns 10,000 us 10 ms 20x datacenter roundtrip
Read 1 MB sequentially from 1 Gbps 10,000,000 ns 10,000 us 10 ms 40x memory, 10X SSD
Read 1 MB sequentially from disk 30,000,000 ns 30,000 us 30 ms 120x memory, 30X SSD
Send packet CA->Netherlands->CA 150,000,000 ns 150,000 us 150 ms
Notes
-----
1 ns = 10^-9 seconds
1 us = 10^-6 seconds = 1,000 ns
1 ms = 10^-3 seconds = 1,000 us = 1,000,000 ns
Handy metrics based on numbers above:
Common system design interview questions, with links to resources on how to solve each.
Articles on how real world systems are designed.
Source: Twitter timelines at scale
Don't focus on nitty gritty details for the following articles, instead:
Architectures for companies you are interviewing with.
Questions you encounter might be from the same domain.
Looking to add a blog? To avoid duplicating work, consider adding your company blog to the following repo:
Interested in adding a section or helping complete one in-progress? Contribute!
Uznania i źródła są dostępne w niniejszym repozytorium.
Specjalne podziękowania dla:
Zachęcam do kontaktu ze mną w celu omówienia wszelkich problemów, pytań lub sugestii.
Informacje kontaktowe znajdziesz na mojej stronie GitHub
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).
Copyright 2017 Donne Martin
Creative Commons Attribution 4.0 International License (CC BY 4.0)
http://creativecommons.org/licenses/by/4.0/
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