Musings on Secondary Indexing in HBase
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The document discusses secondary indexing in HBase, addressing challenges in efficiently performing lookups deeper than row keys and proposing various implementations like Omid and Percolator. It highlights the need for flexible indexing strategies tailored to specific data use cases, pointing out considerations for built-in vs. external libraries, index cardinality, and client-side management of indexes. The author emphasizes the importance of allowing users to choose their indexing methods while maintaining ease of use and performance.
Musings on Secondary Indexing in HBase
- 1. Jesse Yates Salesforce.com Secondary Indexing the discussion so far…. 9/11/12 HBase Pow-wow
- 2. What is it?
- 3. Problem • HBase rows are multi-dimensional – Only sorted on the row key • How do you efficiently lookup deeper into the row key?
- 4. Example Row Family Qualifier Timestamp value 1 Name First 0 Babe 1 Name Last 0 Ruth How do we find all people with the last name ‘Ruth’? Full table scan!
- 5. Indexing! Row Family Qualifier Timestamp Value Ruth Name Last 0 1 Store the property we need to search for as the primary key • pointer back to the primary row • fast lookup - O(lg(n))
- 6. Use Cases • Point lookups – Volume of data influences usefulness of index • Let user decide if they need to use an index • Scan lookup – WHERE age > 16
- 8. Omid Full transactional support Centralized oracle
- 9. Lily WAL implementation on top of HBase 100-500 writes/sec
- 10. Percolator Full transactions Distributed, optimistic locking ~10 sec latencies possible
- 11. Culvert Async Dead project, incomplete
- 12. http://jyates.github.com/2012/07/0 9/consistent-enough-secondary- indexes.html Client-side coordinated index Use timestamps to coordinate Not yet implemented
- 13. Trend Micro Implementation Still just POC ???
- 14. Solr/Lucene Standard Lucene library bolted on HBase Not commonly used Lots of formats/codecs already written
- 15. Considerations for HBase What do we need to do?
- 16. Built-in vs. external library vs. semi-supported (e.g. security)
- 17. Which should I use?? • HBase experts write a single ‘right’ impl • Officially endorse a ‘correct’ version • What changes do we need to make • How close to the core is the project – Written in everywhere – hbase-index module – External library
- 18. Async vs. Synchronous vs. Transactional
- 19. Key Observation “Secondary indexing is inherently an easier problem than full transactions… secondary index updates are idempotent.” - Lars Hofhansl
- 20. Async vs. Synchronous vs.Transactional • We don’t need full transactions – Transactions are slow – Transactions fail with increasing probability as number of servers increases • Optionally async or sync – Async • Inherently ‘dirty’ index • How does index cleanup work? – Inherently different for each type
- 21. Locality
- 22. Where’s my data? • Extra columns vs. index table • HBase Region-pinning – Has to be best-effort or will decrease availability – Helps minimize RPC overhead – Cross-table region-pinning – Needs a coprocessor hook to be useful • HDFS block allocation – Keep index and data blocks on same HDFS node
- 24. How much data are we talking? “Seems like there are 3 categories of sparseness: 1. sparse indexes (like ipAddress) where a per-table approach is more efficient for reads 1. dense indexes (like eventType) where there are likely values of every index key on each region 1. very dense indexes (like male/female) where you should just be doing a table scan anyway” - Matt Corgan (9/10/12)
- 25. Impact on implementation • Need a lot of knowledge of data to pick the right kind of index – User knows their data, let them do the hard work of picking indexes
- 26. Pluggability
- 27. Everyone’s got an impl already • We need to make HBase flexible enough to support (most) current indexing formats with minimal overhead for switching – Lucene style Codec/CodecProvider?
- 28. Client-interface
- 29. What should it look like? • Minimal changes to the top-level interfaces – Add a single new flag? – Configuration based? • Enough that the user gets to be smart about what should be used – We can’t get all cases right – just provide building blocks • Automatically use an index? • Scanner/Filter style use?
- 30. Properties for the client • Should the user even see the index lookups? • ACID? • Ordering of results? – Support the current sorted order? – Batch lookup? • Implications on current features – Replication – splitting
- 31. Schema(less) • Schema enforced? – Rigid usage of index matching an expected schema? – Schema table? Reserved schema columns?.META.? • Schema-less – Let the user apply whatever they think and use only what actually works • Best-effort – Use client-hinted schema and try to apply all the known indexes
- 32. My random thoughts…. • Client-side managed indexes are efficient – Minimal RPC overhead • Cleanup is async to client and rarely misses – Solves the cross-region/server problem • Region-pinning is a nice-to-have optimization – Scales without concern for locality – Flexible enough to support custom codecs – Can be built to provide server-side optimizations • Locality aware indexes to minimize RPCs
- 33. Discussion!