NOSQL별 Best Case 정리

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Kristof Kovacs라는 아저씨고 컨설턴트라고 하는데 실력 있어보임 

Software architect, consultant

아래는 짤방

이 아저씨 정리 내용임  

혹시 링크 깨지면 귀찮으니 내용도 copy and paste

Redis (V3.0RC)

Written in: C

Main point: Blazing fast

License: BSD

Protocol: Telnet-like, binary safe

Disk-backed in-memory database,

Dataset size limited to computer RAM (but can span multiple machines' RAM with clustering)

Master-slave replication, automatic failover

Simple values or data structures by keys

but complex operations like ZREVRANGEBYSCORE.

INCR & co (good for rate limiting or statistics)

Bit operations (for example to implement bloom filters)

Has sets (also union/diff/inter)

Has lists (also a queue; blocking pop)

Has hashes (objects of multiple fields)

Sorted sets (high score table, good for range queries)

Lua scripting capabilities (!)

Has transactions (!)

Values can be set to expire (as in a cache)

Pub/Sub lets one implement messaging

Best used: For rapidly changing data with a foreseeable database size (should fit mostly in memory).

For example: To store real-time stock prices. Real-time analytics. Leaderboards. Real-time communication. And wherever you used memcached before.

MongoDB (2.6.7)

Written in: C++

Main point: Retains some friendly properties of SQL. (Query, index)

License: AGPL (Drivers: Apache)

Protocol: Custom, binary (BSON)

Master/slave replication (auto failover with replica sets)

Sharding built-in

Queries are javascript expressions

Run arbitrary javascript functions server-side

Better update-in-place than CouchDB

Uses memory mapped files for data storage

Performance over features

Journaling (with --journal) is best turned on

On 32bit systems, limited to ~2.5Gb

Text search integrated

GridFS to store big data + metadata (not actually an FS)

Has geospatial indexing

Data center aware

Best used: If you need dynamic queries. If you prefer to define indexes, not map/reduce functions. If you need good performance on a big DB. If you wanted CouchDB, but your data changes too much, filling up disks.

For example: For most things that you would do with MySQL or PostgreSQL, but having predefined columns really holds you back.

Cassandra (2.0)

Written in: Java

Main point: Store huge datasets in "almost" SQL

License: Apache

Protocol: CQL3 & Thrift

CQL3 is very similar SQL, but with some limitations that come from the scalability (most notably: no JOINs, no aggregate functions.)

CQL3 is now the official interface. Don't look at Thrift, unless you're working on a legacy app. This way, you can live without understanding ColumnFamilies, SuperColumns, etc.

Querying by key, or key range (secondary indices are also available)

Tunable trade-offs for distribution and replication (N, R, W)

Data can have expiration (set on INSERT).

Writes can be much faster than reads (when reads are disk-bound)

Map/reduce possible with Apache Hadoop

All nodes are similar, as opposed to Hadoop/HBase

Very good and reliable cross-datacenter replication

Distributed counter datatype.

You can write triggers in Java.

Best used: When you need to store data so huge that it doesn't fit on server, but still want a friendly familiar interface to it.

For example: Web analytics, to count hits by hour, by browser, by IP, etc. Transaction logging. Data collection from huge sensor arrays.

ElasticSearch (0.20.1)

Written in: Java

Main point: Advanced Search

License: Apache

Protocol: JSON over HTTP (Plugins: Thrift, memcached)

Stores JSON documents

Has versioning

Parent and children documents

Documents can time out

Very versatile and sophisticated querying, scriptable

Write consistency: one, quorum or all

Sorting by score (!)

Geo distance sorting

Fuzzy searches (approximate date, etc) (!)

Asynchronous replication

Atomic, scripted updates (good for counters, etc)

Can maintain automatic "stats groups" (good for debugging)

Still depends very much on only one developer (kimchy).

Best used: When you have objects with (flexible) fields, and you need "advanced search" functionality.

For example: A dating service that handles age difference, geographic location, tastes and dislikes, etc. Or a leaderboard system that depends on many variables.

CouchDB (V1.2)

Written in: Erlang

Main point: DB consistency, ease of use

License: Apache

Protocol: HTTP/REST

Bi-directional (!) replication,

continuous or ad-hoc,

with conflict detection,

thus, master-master replication. (!)

MVCC - write operations do not block reads

Previous versions of documents are available

Crash-only (reliable) design

Needs compacting from time to time

Views: embedded map/reduce

Formatting views: lists & shows

Server-side document validation possible

Authentication possible

Real-time updates via '_changes' (!)

Attachment handling

thus, CouchApps (standalone js apps)

Best used: For accumulating, occasionally changing data, on which pre-defined queries are to be run. Places where versioning is important.

For example: CRM, CMS systems. Master-master replication is an especially interesting feature, allowing easy multi-site deployments.

Accumulo (1.4)

Written in: Java and C++

Main point: A BigTable with Cell-level security

License: Apache

Protocol: Thrift

Another BigTable clone, also runs of top of Hadoop

Originally from the NSA

Cell-level security

Bigger rows than memory are allowed

Keeps a memory map outside Java, in C++ STL

Map/reduce using Hadoop's facitlities (ZooKeeper & co)

Some server-side programming

Best used: If you need to restict access on the cell level.

For example: Same as HBase, since it's basically a replacement: Search engines. Analysing log data. Any place where scanning huge, two-dimensional join-less tables are a requirement.

HBase (V0.92.0)

Written in: Java

Main point: Billions of rows X millions of columns

License: Apache

Protocol: HTTP/REST (also Thrift)

Modeled after Google's BigTable

Uses Hadoop's HDFS as storage

Map/reduce with Hadoop

Query predicate push down via server side scan and get filters

Optimizations for real time queries

A high performance Thrift gateway

HTTP supports XML, Protobuf, and binary

Jruby-based (JIRB) shell

Rolling restart for configuration changes and minor upgrades

Random access performance is like MySQL

A cluster consists of several different types of nodes

Best used: Hadoop is probably still the best way to run Map/Reduce jobs on huge datasets. Best if you use the Hadoop/HDFS stack already.

For example: Search engines. Analysing log data. Any place where scanning huge, two-dimensional join-less tables are a requirement.

Hypertable (0.9.6.5)

Written in: C++

Main point: A faster, smaller HBase

License: GPL 2.0

Protocol: Thrift, C++ library, or HQL shell

Implements Google's BigTable design

Run on Hadoop's HDFS

Uses its own, "SQL-like" language, HQL

Can search by key, by cell, or for values in column families.

Search can be limited to key/column ranges.

Sponsored by Baidu

Retains the last N historical values

Tables are in namespaces

Map/reduce with Hadoop

Best used: If you need a better HBase.

For example: Same as HBase, since it's basically a replacement: Search engines. Analysing log data. Any place where scanning huge, two-dimensional join-less tables are a requirement.

Couchbase (ex-Membase) (2.0)

Written in: Erlang & C

Main point: Memcache compatible, but with persistence and clustering

License: Apache

Protocol: memcached + extensions

Very fast (200k+/sec) access of data by key

Persistence to disk

All nodes are identical (master-master replication)

Provides memcached-style in-memory caching buckets, too

Write de-duplication to reduce IO

Friendly cluster-management web GUI

Connection proxy for connection pooling and multiplexing (Moxi)

Incremental map/reduce

Cross-datacenter replication

Best used: Any application where low-latency data access, high concurrency support and high availability is a requirement.

For example: Low-latency use-cases like ad targeting or highly-concurrent web apps like online gaming (e.g. Zynga).

Scalaris (0.5)

Written in: Erlang

Main point: Distributed P2P key-value store

License: Apache

Protocol: Proprietary & JSON-RPC

In-memory (disk when using Tokyo Cabinet as a backend)

Uses YAWS as a web server

Has transactions (an adapted Paxos commit)

Consistent, distributed write operations

From CAP, values Consistency over Availability (in case of network partitioning, only the bigger partition works)

Best used: If you like Erlang and wanted to use Mnesia or DETS or ETS, but you need something that is accessible from more languages (and scales much better than ETS or DETS).

For example: In an Erlang-based system when you want to give access to the DB to Python, Ruby or Java programmers.

Aerospike (3.4.1)

Written in: C

Main point: Speed, SSD-optimized storage

License: License: AGPL (Client: Apache)

Protocol: Proprietary

Cross-datacenter replication is commercially licensed

Very fast access of data by key

Uses SSD devices as a block device to store data (RAM + persistence also available)

Automatic failover and automatic rebalancing of data when nodes or added or removed from cluster

User Defined Functions in LUA

Cluster management with Web GUI

Has complex data types (lists and maps) as well as simple (integer, string, blob)

Secondary indices

Aggregation query model

Data can be set to expire with a time-to-live (TTL)

Large Data Types

Best used: Any application where low-latency data access, high concurrency support and high availability is a requirement.

For example: Storing massive amounts of profile data in online advertising or retail Web sites.

RethinkDB (2.1)

Written in: C++

Main point: JSON store that streams updates

License: License: AGPL (Client: Apache)

Protocol: Proprietary

JSON document store

Javascript-based query language, "ReQL"

ReQL is functional, if you use Underscore.js it will be quite familiar

Sharded clustering, replication built-in

Data is JOIN-able on references

Handles BLOBS

Geospatial support

Multi-datacenter support

Best used: Applications where you need constant real-time upates.

For example: Displaying sports scores on various displays and/or online. Monitoring systems. Fast workflow applications.

Riak (V1.2)

Written in: Erlang & C, some JavaScript

Main point: Fault tolerance

License: Apache

Protocol: HTTP/REST or custom binary

Stores blobs

Tunable trade-offs for distribution and replication

Pre- and post-commit hooks in JavaScript or Erlang, for validation and security.

Map/reduce in JavaScript or Erlang

Links & link walking: use it as a graph database

Secondary indices: but only one at once

Large object support (Luwak)

Comes in "open source" and "enterprise" editions

Full-text search, indexing, querying with Riak Search

In the process of migrating the storing backend from "Bitcask" to Google's "LevelDB"

Masterless multi-site replication and SNMP monitoring are commercially licensed

Best used: If you want something Dynamo-like data storage, but no way you're gonna deal with the bloat and complexity. If you need very good single-site scalability, availability and fault-tolerance, but you're ready to pay for multi-site replication.

For example: Point-of-sales data collection. Factory control systems. Places where even seconds of downtime hurt. Could be used as a well-update-able web server.

VoltDB (2.8.4.1)

Written in: Java

Main point: Fast transactions and rapidly changing data

License: AGPL v3 and proprietary

Protocol: Proprietary

In-memory relational database.

Can export data into Hadoop

Supports ANSI SQL

Stored procedures in Java

Cross-datacenter replication

Best used: Where you need to act fast on massive amounts of incoming data.

For example: Point-of-sales data analysis. Factory control systems.

Kyoto Tycoon (0.9.56)

Written in: C++

Main point: A lightweight network DBM

License: GPL

Protocol: HTTP (TSV-RPC or REST)

Based on Kyoto Cabinet, Tokyo Cabinet's successor

Multitudes of storage backends: Hash, Tree, Dir, etc (everything from Kyoto Cabinet)

Kyoto Cabinet can do 1M+ insert/select operations per sec (but Tycoon does less because of overhead)

Lua on the server side

Language bindings for C, Java, Python, Ruby, Perl, Lua, etc

Uses the "visitor" pattern

Hot backup, asynchronous replication

background snapshot of in-memory databases

Auto expiration (can be used as a cache server)

Best used: When you want to choose the backend storage algorithm engine very precisely. When speed is of the essence.

For example: Caching server. Stock prices. Analytics. Real-time data collection. Real-time communication. And wherever you used memcached before.