HBaseCon 2012 | Low Latency OLAP with HBase - Cosmin Lehene, Adobe

2. What we needed … and built  OLAP Semantics  Low Latency Ingestion  High Throughput  Real-time Query API  Not hardcoded to web analytics or x-, y-, z- analytics, but extensible© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 2

4. OLAP 101 – Queries example Date Countr City OS Browser Sale y 2012-05-21 USA NY Windows FF 0.0 2012-05-21 USA NY Windows FF 10.0 2012-05-22 USA SF OSX Chrome 25.0 2012-05-22 Canada Ontario Linux Chrome 0.0 2012-05-23 USA Chicago OSX Safari 15.0 5 visits, 2 4 cities: 3 OS-es 3 browsers 50.0 3 days countries NY: 2 Win: 2 FF: 2 3 sales USA: 4 SF: 1 OSX: 2 Chrome:2 Canada: 1© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 4

5. OLAP 101 – Queries example  Rolling up to country level: Country visits sales SELECT COUNT(visits), SUM(sales) USA 4 $50 GROUP BY country Canada 1 0  “Slicing” by browser Country visits sales SELECT COUNT(visits), SUM(sales) USA 2 $10 GROUP BY country Canada 0 0 HAVING browser = “FF”  Top browsers by sales Browser sales visits SELECT SUM(sales), COUNT(visits) Chrome $25 2 GROUP BY browser Safari $15 1 ORDER BY sales FF $10 2© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 5

6. OLAP – Runtime Aggregation vs. Pre-aggregation  Aggregate at runtime  Pre-aggregate  Most flexible  Fast  Fast – scatter gather  Efficient – O(1)  Space efficient  High throughput  But  But  I/O, CPU intensive  More effort to process (latency)  slow for larger data  Combinatorial explosion (space)  low throughput  No flexibility© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 6

7. Pre-aggregation  Data needs to be summarized  Can’t visualize 1B data points (no, not even with Retina display)  Difficult to comprehend correlations among more than 3 dimensions  Not all dimension groups are relevant  Index on a needed basis (view selection problem)  Runtime aggregation == TeraSort for every query?  Pre-aggregate to reduce cardinality© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 7

8. SaasBase  We tune both  pre-aggregation level vs. runtime post-aggregation  (ingestion speed + space ) vs. (query speed)  Think materialized views from RDBMS© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 8

14. Ingestion throughput vs. latency  Historical data (large batches)  Optimize for throughput  Increments (latest data, smaller)  Optimize for latency© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 14

15. Large, granular input strategies  Slow listing in HDFS  Archive processed files  Filtering input  FileDateFilter (log name patterns: log-YYYY-MM-dd-HH.log)  TableInputFormat start/stop row  File Index in HBase (track processed/new files)  Map tasks overhead - stitching input splits  400K files => 400K map tasks => overhead, slow reduce copy  CombineFileInputFormat – 2GB-splits => 500 splits for 1TB  FixedMappersTableInputFormat (e.g. 5-region splits)© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 15

16. Ingestion – Bulk Import  HFileOutputFormat (HFOF)  100s X faster than HBase API  No need to recover from failed jobs  No unnecessary load on machines * No shuffle - global reduce order required!  e.g. first reduce key needs to be in the first region, last one in the last region  Watch for uneven partitions© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 16

17. HFOF – FileSizeDatePartitioner  1 partition(reduce) / day for initial import  Uneven reduce (partitions) due to data growth over time  Reduce k: 2010-12-04 = 500MB  Reduce n: 2012-05-22 = 5GB => slow and will result in a 5GB region  Balance reduce buckets based on input file sizes and the reduce key  Generate sub-partitions based on predefined size (e.g. 1GB)© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 17

19. Processing  Processing involves reading the Input (files, tables, events), pre- aggregating it (reducing cardinality) and generating tables that can be queried in real-time  1 year: 1B events => 100B data points indexed  Query => scan 365 data points (e.g. daily page views)  Processing could be either MR or real-time (e.g. Storm)© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 19

20. Processing for OLAP semantics  GROUP BY (process, query)  COUNT, SUM, AVG, etc. (process, query)  SORT (process, query)  HAVING (mostly query, can define pre-process constraints)© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 20

23. Processing Performance  read, map, partition, combine, copy, sort, reduce, write  Read:  Scan.setCaching() (I/O ~ buffer)  Scan.setBatching() (avoid timeouts for abnormal input, e.g. 1M hits/visit)  Even region distribution across cluster (distributes CPU, I/O)  Map:  No unnecessary transformations: Bytes.toString(bytes) + Bytes.toBytes(string) (CPU)  Avoid GC : new X() (CPU, Memory)  Avoid system calls (context switching)  Stripping unnecessary data (I/O)© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 23

24. Processing Performance  Hot (in memory) vs. Cold (on disk, on network) data  Minimize I/O from disk/network  Single shot MR job: SuperProcessor  Emit all groups from one map() call  Incremental processing  Data format YYYY-MM-DD prefixed rowkey (HH:mm for more granularity)© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 24

27. Indexing - Why  Example: top 10 cities  ~50K [country, city] combinations per day  Top 10 cities for 1 year =>  365 (days) X 50K ~=15M data points scanned  If you add gender => 30M  If you add Device, OS, Browser …  Might compress well, but think about the environment  How much energy would you spend for just top 10 cities? * Image from: http://my.neutralexistence.com/images/Green-Earth.jpg© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 27

28. Indexing with HBase “10” < “2” GROUP BY year, month, country, city ORDER BY visits DESC LIMIT 10  Lexicographic sorting 2012/05/USA/0000000000/ 2012/05/USA/4294961296/San Francisco = 1000 visits* 2012/05/USA/4294961396/New York = 900 visits* . . . 2012/05/USA/9999999999/  scan “t” startrow => “2012/05/USA/”, limit => 10 * Padding numbers for lexicographic sorting: 1000 -> Long.MAX_VALUE – 1000 = 4294961296© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 28

29. Query Engine  Always reads indexed, compact data  Query parsing  Scan strategy  Single vs. multiple scans  Start/stop rows (prefixes, index positions, etc.)  Index selection (volatile indexes with incremental processing)  Deserialization  Post-aggregation, sorting, fuzzy-sorting etc.  Paging  Custom dimension/metric class loading© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 29

30. Conclusions  OLAP semantics on a simple data model  Data as first class citizen  Domain Specific “Language” for Dimensions, Metrics, Aggregations  Tunable performance, resource allocation  Framework for vertical analytics systems© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 30

31. Thank you! Cosmin Lehene @clehene http://hstack.org Credits: Andrei Dragomir Adrian Muraru Andrei Dulvac Raluca Podiuc Tudor Scurtu Bogdan Dragu Bogdan Drutu© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 31

33. OLAP 101 - Rollup Countr Visits Sale y USA 4 $50 Canada 1 $0  Rollup: SELECT COUNT(visits), SUM(sales) GROUP BY country© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 33

34. OLAP 101 - Slicing Date Countr City OS Browser Sale y 2012-03-02 USA NY Windows FF 0.0 2012-03-02 USA NY Windows FF 10.0 2012-03-03 USA S OSX Chrome 25.0 2012-03-03 Canada Ontario Linux Chrome 0.0 2012-03-04 USA Chicago OSX Safari 15.0 5 visits, 2 4 cities: 3 OS-es 3 browsers 50.0 3 days countries NY: 2 Win: 2 FF: 2 3 sales USA: 4 SF: 1 OSX: 2 Chrome:2 Canada: 1  Filter or Segment or Slice (WHERE or HAVING)© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 34

35. OLAP 101 – Sorting, TOP n Date Countr City OS Browser Sale y Chrome $25 Safari $15 Firefox $10  SELECT SUM(sales) as total GROUP BY browser ORDER BY total© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 35

HBaseCon 2012 | Low Latency OLAP with HBase - Cosmin Lehene, Adobe

Cloudera, Inc.

HBaseCon 2012 | Low Latency OLAP with HBase - Cosmin Lehene, Adobe