Secondary Indexing

the discussion so far….

9/11/12 HBase Pow-wow

Jesse YatesSalesforce.com

What is it?

Problem

• HBase rows are multi-dimensional– Only sorted on the row key

• How do you efficiently lookup deeper into the row key?

ExampleRow 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!

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))

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

Implementations

Omid

Full transactional supportCentralized oracle

Lily

WAL implementation on top of HBase100-500 writes/sec

Percolator

Full transactionsDistributed, optimistic locking

~10 sec latencies possible

Culvert

AsyncDead project, incomplete

http://jyates.github.com/2012/07/09/consistent-enough-secondary-indexes.html

Client-side coordinated indexUse timestamps to coordinate

Not yet implemented

Trend Micro Implementation

Still just POC???

Solr/Lucene

Standard Lucene library bolted on HBaseNot commonly used

Lots of formats/codecs already written

Considerations for HBase

What do we need to do?

Built-in vs. external library vs.

semi-supported (e.g. security)

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

Async vs. Synchronous vs.

Transactional

Key Observation

“Secondary indexing is inherently an easier problem than full transactions… secondary index updates are idempotent.”

- Lars Hofhansl

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

Locality

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

Index Cardinality

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

2. dense indexes (like eventType) where there are likely values of every index key on each region

3. very dense indexes (like male/female) where you should just be doing a table scan anyway”

- Matt Corgan (9/10/12)

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

Pluggability

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?

Client-interface

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?

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

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

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

Discussion!