Comparing Scalable NOSQL Databases
Functionalities and Measurements
Dory Thibault UCL
Contact : [email protected] Sponsor : Euranova
Website : nosqlbenchmarking.com February 15, 2011
Overview of the databases Methodology Results Summary and conclusion
Clari cations
As a lot of people who read those slides did not get the oral explanations that MUST go with it, here are a few words of warning :
All the databases were used with default con gurations, I will post them soon on nosqlbenchmarking.com
No index was set manually, doing so could have a big impact on performances
Don't jump too fast on the conclusions, it would be WRONG to say that Cassandra is very good and that HBase sucks.
The Cassandra implementation of MapReduce seems to be
buggy and do not scale. There must be something wrong with my HBase con guration, HBase is known to run gigantic
cluster without problems.
Overview of the databases Methodology Results Summary and conclusion
Clari cations
Also keep in mind that a benchmark is always biased by the chosen methodology so :
The way I store data in each database could have an impact on the performances
The summary about the results should not be taken in an absolute way, especially the rst one. When I say Good or
Bad it is in THIS particular case. Moreover raw results are not the most important, scalability is very important too. So good performances for Cassandra MapReduce but without
scalability is NOT good.
The data set is too small, I'm testing cache performances (but it is the same for all of the databases)
Overview of the databases Methodology Results Summary and conclusion
Motivation
YCSB
Yahoo! Cloud Servicing Benchmark is the best known noSQL bench- marking application so why make another one?
YCSB uses data generated from statistical distributions instead of real data
YCSB only focuses on read/write/update/scan performances YCSB results for elasticity are not conclusive
Idea
Data and use case inspired by a concrete case : Wikipedia Test read/update performances
Test MapReduce performances by computing an inverted
Overview of the databases Methodology Results Summary and conclusion
Cassandra 0.6.10 HBase 0.20.6 mongoDB 1.6.5 Riak 0.14
Cassandra 0.6.10
Overview
Cassandra is a fully distributed column oriented data store that pro- vides a MapReduce implementation using Hadoop.
All the nodes in the cluster play the same role
The data (existing and new) are sharded automatically among the nodes
The developer can choose the consistency level for each request
Overview of the databases Methodology Results Summary and conclusion
Cassandra 0.6.10 HBase 0.20.6 mongoDB 1.6.5 Riak 0.14
HBase 0.20.6
Overview
HBase is a column oriented database that aims to provide low latency requests on top of Hadoop HDFS
An HBase cluster uses several kinds of servers :
HDFS needs at least one namenode and several datanodes HBase needs a ZooKeeper cluster, a master and several regionservers
The requests must be made to the master(s)
On the HDFS level, existing data are not sharded automatically but new data are
On the HBase level, the data are divided into regions that are sharded automatically across regionservers
Overview of the databases Methodology Results Summary and conclusion
Cassandra 0.6.10 HBase 0.20.6 mongoDB 1.6.5 Riak 0.14
mongoDB 1.6.5
Overview
mongoDB is a document oriented database that stores JSON dic- tionnaries. It provides auto sharding and a MapReduce implemen- tation.
A mongoDB cluster is made of several kinds of servers :
The shard servers that store data
The con guration servers that store the con guration The router servers that receive and route the requests
Existing and new data are sharded automatically MapReduce can only use one thread by server
Overview of the databases Methodology Results Summary and conclusion
Cassandra 0.6.10 HBase 0.20.6 mongoDB 1.6.5 Riak 0.14
Riak 0.14
Overview
Riak is a fully distributed key/bucket store with an implementation of MapReduce.
Buckets can store the data directly or be a link to another bucket
All the nodes in the cluster play the same role
The data (existing and new) are sharded automatically amongs the nodes
The developer can choose the consistency level for each request
Overview of the databases Methodology Results Summary and conclusion
The data used The client
The methodology
The data
Wikipedia export
20.000 pages downloaded from Wikipedia Every document is in XML format All documents sum up to 620Mo
Each document is associated to a single integer ID Insertions
Each document is inserted only once during the whole benchmark
Overview of the databases Methodology Results Summary and conclusion
The data used The client
The methodology
The client
Overview
Fully random requests
Acts as a perfect load balancer
The proportion of updates can be speci ed
Speci c parts : read/write/update and MapReduce Updates
The updates simply concatenate the string \1" at the end of the article.
Overview of the databases Methodology Results Summary and conclusion
The data used The client
The methodology
MapReduce
Overview
MapReduce is used to build a reverse index for a given keyword.
The reverse index is a list of pairs made of : ID : the ID of the article if Count 6= 0
Count : the number of occurrences of the keyword in this article
Justi cation
This kind of computation implies that all the documents are crawled and take advantage of the speci cations of MapReduce
Overview of the databases Methodology Results Summary and conclusion
The data used The client
The methodology
The methodology
1 Start up a clean cluster of size 3 and insert all the documents
2 Choose a total number of requests, a read percentage and starts the benchmark
3 Wait one minute and starts the benchmark again
4 Wait ve minutes and starts the benchmark again
5 Start the MapReduce benchmark
6 Add a new node to the cluster and wait for it to be ready then restart immediately the bench with the new node's IP in the list
7 Jump to 3 until there are no more computer to add to the cluster
Overview of the databases Methodology Results Summary and conclusion
Read/update results
Overview of the databases Methodology Results Summary and conclusion
Read/update results without HBase
Overview of the databases Methodology Results Summary and conclusion
MapReduce performance
Overview of the databases Methodology Results Summary and conclusion
The HBase case
Veri cations made :
Checked the logs : nothing seemed problematic
HDFS level : running the balancer with a very low threshold distributed the blocks evenly but without any impact on the performances
HBase level : the regions where always nearly evenly distributed across the regionservers
The number of rows did not change and the content of each row was correct
Overview of the databases Methodology Results Summary and conclusion
Summary of raw performances
DB read/update performances MapReduce performances
Cassandra Good Very Good
HBase Bad / N.A. Average / N.A
mongoDB Good Poor but scalable
Riak Poor / unstable Average but scalable
Overview of the databases Methodology Results Summary and conclusion
Summary of scalability
Going from 3 to 8 servers is a 266% increase in capacity, here are the observed increases in performances :
DB read/update MapReduce
Cassandra 153% 112%
HBase 11% 43%
mongoDB 145% 211%
Riak 74% 189%
Riak 7 nodes max 155% 168%
Overview of the databases Methodology Results Summary and conclusion
Conclusion and future work
Conclusion
The elastic gain seems more apparent than with YCSB but not linear either
It is worth testing MapReduce performances as the results vary a lot between databases for both raw and scalability performances
Future work
This is still a work in progress :
Applying this benchmark to other databases (Terrastore, Voldemort, Scalaris ...)
Overview of the databases Methodology Results Summary and conclusion
Questions and remarks
Any questions or remarks?
