Comparing noSQL databases : benchmark

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. Motivation Overview of the databases Methodology Results Summary and conclusion Clari

3. 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

4. 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

5. guration, HBase is known to run gigantic cluster without problems. 2 / 20

6. Motivation Overview of the databases Methodology Results Summary and conclusion Clari

7. 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

8. 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 www.nosqlbenchmarking.com 3 / 20

9. Motivation 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 search index 4 / 20

10. Motivation Cassandra 0.6.10 Overview of the databases HBase 0.20.6 Methodology mongoDB 1.6.5 Results Riak 0.14 Summary and conclusion Cassandra 0.6.10 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

11. Motivation Cassandra 0.6.10 Overview of the databases HBase 0.20.6 Methodology mongoDB 1.6.5 Results Riak 0.14 Summary and conclusion 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 datanodes and several HBase needs a ZooKeeper cluster master , a 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

12. Motivation Cassandra 0.6.10 Overview of the databases HBase 0.20.6 Methodology mongoDB 1.6.5 Results Riak 0.14 Summary and conclusion mongoDB 1.6.5 Overview mongoDB is a document oriented database that stores JSON dic- tionnaries. 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 con

13. guration servers that store the con

14. 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 7 / 20

15. Motivation Cassandra 0.6.10 Overview of the databases HBase 0.20.6 Methodology mongoDB 1.6.5 Results Riak 0.14 Summary and conclusion 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

16. Motivation Overview of the databases The data used Methodology The client Results The methodology Summary and conclusion 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 9 / 20

17. Motivation Overview of the databases The data used Methodology The client Results The methodology Summary and conclusion The client Overview Fully random requests Acts as a perfect load balancer The proportion of updates can be speci

18. ed Speci

19. c parts : read/write/update and MapReduce Updates The updates simply concatenate the string 1" at the end of the article. 10 / 20

20. Motivation Overview of the databases The data used Methodology The client Results The methodology Summary and conclusion 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

21. cation This kind of computation implies that all the documents are crawled and take advantage of the speci

22. cations of MapReduce 11 / 20

23. Motivation Overview of the databases The data used Methodology The client Results The methodology Summary and conclusion 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

24. 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

25. Motivation Overview of the databases Methodology Results Summary and conclusion Read/update results 13 / 20

26. Motivation Overview of the databases Methodology Results Summary and conclusion Read/update results without HBase 14 / 20

27. Motivation Overview of the databases Methodology Results Summary and conclusion MapReduce performance 15 / 20

28. Motivation Overview of the databases Methodology Results Summary and conclusion The HBase case Veri

29. 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 16 / 20

30. Motivation 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 17 / 20

31. Motivation 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% 18 / 20

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

33. Motivation Overview of the databases Methodology Results Summary and conclusion Questions and remarks Any questions or remarks? 20 / 20

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