Comparing Scalable NOSQL Databases

Transcription

1 Comparing Scalable NOSQL Databases Functionalities and Measurements Dory Thibault UCL Contact : thibault.dory@student.uclouvain.be Sponsor : Euranova Website : nosqlbenchmarking.com February 15, 2011

2 Clarications Motivation 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 congurations, 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 conguration, HBase is known to run gigantic cluster without problems. 2 / 20

3 Clarications Motivation 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) I will add soon a written analysis and a self critic about those results on 3 / 20

4 Motivation YCSB Yahoo! Cloud Servicing Benchmark is the best known nosql benchmarking 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 search index 4 / 20

5 Cassandra Motivation Cassandra HBase mongodb Riak 0.14 Overview Cassandra is a fully distributed column oriented data store that provides 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 5 / 20

6 HBase Motivation Cassandra HBase mongodb Riak 0.14 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 6 / 20

7 Cassandra HBase mongodb Riak 0.14 mongodb Overview mongodb is a document oriented database that stores JSON dictionnaries. It provides auto sharding and a MapReduce implementation. A mongodb cluster is made of several kinds of servers : The shard servers that store data The conguration servers that store the conguration The router servers that receive and route the requests Existing and new data are sharded automatically MapReduce can only use one thread by server 7 / 20

8 Riak 0.14 Motivation Cassandra HBase mongodb 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 8 / 20

9 The data Motivation The data used The client The methodology Wikipedia export 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 9 / 20

10 The client Motivation The data used The client The methodology Overview Updates Fully random requests Acts as a perfect load balancer The proportion of updates can be specied Specic parts : read/write/update and MapReduce The updates simply concatenate the string \1" at the end of the article. 10 / 20

11 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 Justication This kind of computation implies that all the documents are crawled and take advantage of the specications of MapReduce 11 / 20

12 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 12 / 20

13 Read/update results 13 / 20

14 Read/update results without HBase 14 / 20

15 MapReduce performance 15 / 20

16 The HBase case Verications 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 16 / 20

17 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 17 / 20

18 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% 18 / 20

19 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...) Trying with a growing/bigger data set 19 / 20

20 Questions and remarks Any questions or remarks? 20 / 20