Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase vs Membase vs Neo4j comparison
2012-09-17 10:32
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But the differences between NoSQL databases are much bigger than it ever was between one SQL database and another. This means that it is a bigger responsibility on software
architects to choose the appropriate one for a project right at the beginning.
In this light, here is a comparison of Cassandra, Mongodb, CouchDB, Redis, Riak, Membase, Neo4j andHBase:
MongoDB
Writtenin: 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
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 (V1.0)
Writtenin: Erlang & C, some Javascript
Main
point: Fault tolerance
License: Apache
Protocol: HTTP/REST
or custom binary
Tunable trade-offs for distribution and replication (N, R, W)
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 server (beta)
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 Cassandra-like (Dynamo-like), 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 (V1.1.1)
Writtenin: 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)
jQuery library included
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)
Writtenin: 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.
HBase (V0.92.0)
Writtenin: 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
Cascading, hive, and pig source and sink modules
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: Analysing log data.
Neo4j (V1.5M02)
Writtenin: 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: Social relations, public transport links, road maps, network topologies.
Cassandra
Writtenin: Java
Main
point: Best of BigTable and Dynamo
License: Apache
Protocol: Custom,
binary (Thrift)
Tunable trade-offs for distribution and replication (N, R, W)
Querying by column, range of keys
BigTable-like features: columns, column families
Has secondary indices
Writes are much faster than reads (!)
Map/reduce possible with Apache Hadoop
All nodes are similar, as opposed to Hadoop/HBase
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 real time data analysis.
Membase
Writtenin: Erlang & C
Main
point: Memcache compatible, but with persistence and clustering
License: Apache
2.0
Protocol: memcached
plus 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
Very nice cluster-management web GUI
Software upgrades without taking the DB offline
Connection proxy for connection pooling and multiplexing (Moxi)
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).
Of course, all 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. I'll do my best to keep this list updated.
-- Kristof
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