Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase vs Couchbase vs Orient

2017-12-30 11:36:50来源:oschina作者:xiaomin0322人点击

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Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase vs Couchbase vs Orient 博客分类: 数据库
The most popular ones 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)

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.


Classic document and BigTable stores 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.


Graph databases OrientDB (2.0)

Written in: Java
Main point: Document-based graph database
License: Apache 2.0
Protocol: binary, HTTP REST/JSON, or Java API for embedding
Has transactions, full ACID conformity
Can be used both as a document and as a graph database (vertices with properties)
Both nodes and relationships can have metadata
Multi-master architecture
Supports relationships between documents via persistent pointers (LINK, LINKSET, LINKMAP, LINKLIST field types)
SQL-like query language (Note: no JOIN, but there are pointers)
Web-based GUI (quite good-looking, self-contained)
Inheritance between classes. Indexing of nodes and relationships
User functions in SQL or JavaScript
Sharding
Advanced path-finding with multiple algorithms and Gremlin traversal language
Advanced monitoring, online backups are commercially licensed

Best used: For graph-style, rich or complex, interconnected data.


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


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
Clustering, replication, caching, online backup, advanced monitoring and High Availability are commercially licensed

Best used: For graph-style, rich or complex, interconnected data.


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


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.

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.

Of course, all these systems have much more features than what's listed here. I only wanted to list the key points that I base my decisions on. Also, development of all are very fast, so things are bound to change.

http://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs-redis/


http://blog.csdn.net/you_jinjin/article/details/7086428

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