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Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase vs Couchbase vs Neo4j vs Hypertable vs Ela

2013-06-05 17:16 561 查看
http://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs-redis


The most popular ones


MongoDB (2.2)

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

An empty database takes up 192Mb

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.


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 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.


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.


Redis (V2.4)

Written in: C/C++

Main point: Blazing fast

License: BSD

Protocol: Telnet-like

Disk-backed in-memory database,

Currently without disk-swap (VM and Diskstore were abandoned)

Master-slave replication

Simple values or hash tables by keys,

but complex operations like ZREVRANGEBYSCORE.

INCR & co (good for rate limiting or statistics)

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)

Redis 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: Stock prices. Analytics. Real-time data collection. Real-time communication. And wherever you used memcached
before.


Clones of Google's Bigtable


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.


Cassandra (1.2)

Written in: Java

Main point: Best of BigTable and Dynamo

License: Apache

Protocol: Thrift & custom binary CQL3

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

Querying by column, range of keys (Requires indices on anything that you want to search on)

BigTable-like features: columns, column families

Can be used as a distributed hash-table, with an "SQL-like" language, CQL (but no JOIN!)

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

Cross-datacenter replication

Best used: When you write more than you read (logging). If every component of the system must be in Java. ("No one
gets fired for choosing Apache's stuff.")

For example: Banking, financial industry (though not necessarily for financial transactions, but these industries are
much bigger than that.) Writes are faster than reads, so one natural niche is data analysis.


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.


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

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 a different 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.


Special-purpose


Neo4j (V1.5M02)

Written in: Java

Main point: Graph database - connected data

License: GPL, some features AGPL/commercial

Protocol: HTTP/REST (or embedding in Java)

Standalone, or embeddable into Java applications

Full ACID conformity (including durable data)

Both nodes and relationships can have metadata

Integrated pattern-matching-based query language ("Cypher")

Also the "Gremlin" graph traversal language can be used

Indexing of nodes and relationships

Nice self-contained web admin

Advanced path-finding with multiple algorithms

Indexing of keys and relationships

Optimized for reads

Has transactions (in the Java API)

Scriptable in Groovy

Online backup, advanced monitoring and High Availability is AGPL/commercial licensed

Best used: For graph-style, rich or complex, interconnected data. Neo4j is quite different from the others in this
sense.

For example: For searching routes in social relations, public transport links, road maps, or network topologies.


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.


The "long tail"

(Not widely known, but definitely worthy ones)


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.


VoltDB (2.8.4.1)

Written in: Java

Main point: Fast transactions and repidly changing data

License: GPL 3

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.
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