NOSQL har igennem de sidste år, gået sin sejrsgang i alle store virksomheder. Og der er kommet flere teknologier og flere producenter og opensource projekter på gaden… Hele NOSQL økosamfunder er vokset enormt, godt hjulet af verdens bedste referencer, nemlig de mest kendte brands i verden som entydigt lovpriser NOSQL.
NOSQL giver bedre vækst for dig, ifølge verdens største analyse- og konsulenthuse
Dertil kommer f.eks. at flere af verdens mest kendte analyservirksomheder er kommet til den overraskende konklusion at DU SKAL anvende NOSQL teknologi HVIS DU VIL HAVE SUCCESS!… Denne konklusion er sket på basis af virksomheders teknologivalg sammenlignet med tilsvarende konkurrenter, som ikke anvender NOSQL. Konklusionen er, at d skal anvende NOSQL hvis du vil vækste fra 2-40 gange hurtigere end dine konkurrenter.
Hvilken NOSQL database skal man vælge?
De traditionelle SQL databaser er kommet til kort overfor NOSQL. De er for dyre, for langsomme og for usikre, jf. flere af USA’s største analysevirksomheder. SQL producenterne har ellers i de sidste 5 år haft en slags monopol på hvordan vi gemte vore data. Det er slut nu, idet NOSQL er verdens hastigst voksende teknologi og samtidigt den teknologi vi benytte mest til daglig i form af Google, Facebook og specielt Cloud Tjenester. CMS industien er næsten alle igang med implementering af interfaces til NOSQL hvor eller MYSQL har været dominerende.
Problemet har været at selvom SQL er ok til nogle få ting, så er det for langsomt, for dyrt og for usikkert (antallet af sikkerhedshuller).
NOSQL markedet er pt. domineret af Cassandra, Mongodb, Redis, Riak, CouchDB, Membase, Neo4j og HBase. Vi giver her en sammenligning af de teknologier som er motoren i NOSQL databasen og hvilke hovedsprog de er programmeret i samt detaljer om interfaces og generelle holdninger og meninger.
MongoDB
- Skrevet i: C++
- Fordel: Har nogle brugervenlige egenskaber fra SQL. (Query, index)
- Licens: AGPL (Drivers: Apache)
- Protokol: Custom, binary (BSON)
- Master/slave replikering (auto failover med replikeringssæt)
- Sharding indbygget
- Forespørgsler er javascript udtryk
- Kører arbitrære javascript funktioner på server-siden
- Bedre til opdateringer end CouchDB
- Bruger hukommelses-mappede filer for datalagring
- Performance er bedre end antallet af faciliteter
- Journalisering (med –journal) er bedre når den er aktiveret
- På 32 bit systemer er der en begrænsning på ~2.5Gb
- En tom database fylder indtil 192Mb
- GridFS for at gemme BIGDATA og metadata (er faktisk ikke FS)
- Har geospatial indexing
Bruges bedst til: hvis der er behov for dynamiske forespørgsler. Hvis du foretrækker at definere indexes, og ikke map/reduce funktioner. Hvis der er behov for god performance på en stor database. Hvis du gerne ville have CouchDB eller CouchBase, men dine data ændrer dig for tit og fylder meget.
For eksempel: hvis du f.eks. kunne klare dig med MySQL eller PostgreSQL, men det at have prædefinerede koloner holder dig tilbage.
Riak (V1.0)
- Skrevet i: Erlang & C, og lidt Javascript
- Fordel: Fejltolerant
- Licens: Apache
- Protokol: 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)
- 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)
- 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)
- 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.
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
- 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
Best used: When you use the Hadoop/HDFS stack. When you need random, realtime read/write access to BigTable-like data.
For example: For data that’s similar to a search engine’s data
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: Social relations, public transport links, road maps, network topologies.
Cassandra
- Written in: 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
- I admit being a bit biased against it, because of the bloat and complexity it has partly because of Java (configuration, seeing exceptions, etc)
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
- Written in: 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.
Denne artikel er originalt skrevet af: Kristof Kovacs, kkovacs.eu (C) Copyright 2011 by agiludvikling.dk
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