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HBase Read High Availability Using
Timeline-Consistent Region Replicas
Enis Soztutar (enis@hortonworks.com)
Devaraj Das (ddas@hortonworks.com)

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About Us
Enis Soztutar
Committer and PMC member in Apache
HBase and Hadoop since 2007
HBase team @Hortonworks
Twitter @enissoz
Devaraj Das
Committer and PMC member in
Hadoop since 2006
Committer at HBase
Co-founder @Hortonworks

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Outline of the talk
PART I: Use case and semantics
§ CAP recap
§ Use case and motivation
§ Region replicas
§ Timeline consistency
§ Semantics
PART II : Implementation and next steps
§ Server side
§ Client side
§ Data replication
§ Next steps & Summary

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Part I
Use case and semantics

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CAP reCAP
Partition tolerance
Consistency Availability
Pick Two
HBase is CP

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Availability
CAP reCAP
• In a distributed system you cannot NOT have P
• C vs A is about what happens if there is a network
partition!
• A an C are NEVER binary values, always a range
• Different operations in the system can have
different A / C choices
• HBase cannot be simplified as CP
Partition tolerance
Consistency
Pick Two
HBase is CP

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HBase consistency model
For a single row, HBase is strongly consistent within a data center
Across rows HBase is not strongly consistent (but available!).
When a RS goes down, only the regions on that server become
unavailable. Other regions are unaffected.
HBase multi-DC replication is “eventual consistent”
HBase applications should carefully design the schema for correct
semantics / performance tradeoff

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Use cases and motivation
More and more applications are looking for a “0 down time” platform
§ 30 seconds downtime (aggressive MTTR time) is too much
Certain classes of apps are willing to tolerate decreased consistency
guarantees in favor of availability
§ Especially for READs
Some build wrappers around the native API to be able to handle failures of
destination servers
§ Multi-DC: when one server is down in one DC, the client switches to a different one
Can we do something in HBase natively?
§ Within the same cluster?

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Use cases and motivation
Designing the application requires careful tradeoff consideration
§ In schema design since single-row is strong consistent, but no multi-row trx
§ Multi-datacenter replication (active-passive, active-active, backups etc)
It is good to be able to give the application flexibility to pick-and-choose
§ Higher availability vs stronger consistency
Read vs Write
§ Different consistency models for read vs write
§ Read-repair, latest ts-wins vs linearizable updates

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Initial goals
Support applications talking to a single cluster really well
§ No perceived downtime
§ Only for READs
If apps wants to tolerate cluster failures
§ Use HBase replication
§ Combine that with wrappers in the application

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Introducing….
Region Replicas in HBase
Timeline Consistency in HBase

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Region replicas
For every region of the table, there can be more than one replica
§ Every region replica has an associated “replica_id”, starting from 0
§ Each region replica is hosted by a different region server
Tables can be configured with a REGION_REPLICATION parameter
§ Default is 1
§ No change in the current behavior
One replica per region is the “default” or “primary”
§ Only this can accepts WRITEs
§ All reads from this region replica return the most recent data
Other replicas, also called “secondaries” follow the primary
§ They see only committed updates

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Region replicas
Secondary region replicas are read-only
§ No writes are routed to secondary replicas
§ Data is replicated to secondary regions (more on this later)
§ Serve data from the same data files are primary
§ May not have received the recent data
§ Reads and Scans can be performed, returning possibly stale data
Region replica placement is done to maximize availability of any particular
region
§ Region replicas are not co-located on same region servers
§ And same racks (if possible)

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rowkey column:value column:value …
RegionServer
Region
memstore
DataNode
b2
b9 b1
DataNode
b2
b1
DataNode
b1
Client
Read and write
RegionServer

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Page 15
rowkey column:value column:value …
RegionServer
Region
DataNode
b2
b9 b1
DataNode
b2
b1
DataNode
b1
Client
Read and write
memstore
RegionServer
rowkey column:value column:value …
memstore
Region replica
Read only

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TIMELINE Consistency
Introduced a Consistency enum
§ STRONG
§ TIMELINE
Consistency.STRONG is default
Consistency can be set per read operation (per-get or per-scan)
Timeline-consistent read RPCs sent to more than one replica
Semantics is a bit different than Eventual Consistency model

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TIMELINE Consistency
public enum Consistency {
STRONG,
TIMELINE
}
Get get = new Get(row);
get.setConsistency(Consistency.TIMELINE);
...
Result result = table.get(get);
…
if (result.isStale()) {
...
}

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TIMELINE Consistency Semantics
Can be though of as in-cluster active-passive replication
Single homed and ordered updates
§ All writes are handled and ordered by the primary region
§ All writes are STRONG consistency
Secondaries apply the mutations in order
Only get/scan requests to secondaries
Get/Scan Result can be inspected to see whether the result was from
possibly stale data

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19
©
Hortonworks
Inc.
2011
–
2014.
All
Rights
Reserved
TIMELINE Consistency Example
Client1
X=1
Client2
WAL
Data:
Replica_id=0
(primary)
Replica_id=1
Replica_id=2
replicaJon
replicaJon
X=3
WAL
Data:
WAL
Data:
X=1
X=1
Write

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20
©
Hortonworks
Inc.
2011
–
2014.
All
Rights
Reserved
TIMELINE Consistency Example
Client1
X=1
Client2
WAL
Data:
Replica_id=0
(primary)
Replica_id=1
Replica_id=2
replicaJon
replicaJon
X=3
WAL
Data:
WAL
Data:
X=1
X=1
X=1
X=1
X=1
X=1
Read
X=1
Read
X=1
Read

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21
©
Hortonworks
Inc.
2011
–
2014.
All
Rights
Reserved
TIMELINE Consistency Example
Client1
X=1
Client2
WAL
Data:
Replica_id=0
(primary)
Replica_id=1
Replica_id=2
replicaJon
replicaJon
WAL
Data:
WAL
Data:
Write
X=1
X=1
X=2
X=2
X=2

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22
©
Hortonworks
Inc.
2011
–
2014.
All
Rights
Reserved
TIMELINE Consistency Example
Client1
X=1
Client2
WAL
Data:
Replica_id=0
(primary)
Replica_id=1
Replica_id=2
replicaJon
replicaJon
WAL
Data:
WAL
Data:
X=2
X=1
X=2
X=2
X=2
X=2
Read
X=2
Read
X=1
Read

Page
23
©
Hortonworks
Inc.
2011
–
2014.
All
Rights
Reserved
TIMELINE Consistency Example
Client1
X=1
Client2
WAL
Data:
Replica_id=0
(primary)
Replica_id=1
Replica_id=2
replicaJon
replicaJon
WAL
Data:
WAL
Data:
X=2
X=1
X=3
X=2
Write
X=3
X=3

Page
24
©
Hortonworks
Inc.
2011
–
2014.
All
Rights
Reserved
TIMELINE Consistency Example
Client1
X=1
Client2
WAL
Data:
Replica_id=0
(primary)
Replica_id=1
Replica_id=2
replicaJon
replicaJon
WAL
Data:
WAL
Data:
X=2
X=1
X=3
X=2
X=3
X=3
Read
X=2
Read
X=1
Read

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PART II
Implementation and next steps

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Region replicas – recap
Every region replica has an associated “replica_id”, starting from 0
Each region replica is hosted by a different region server
§ All replicas can serve READs
One replica per region is the “default” or “primary”
§ Only this can accepts WRITEs
§ All reads from this region replica return the most recent data

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Updates in the Master
Replica creation
§ Created during table creation
No distinction between primary & secondary replicas
Meta table contain all information in one row
Load balancer improvements
§ LB made aware of replicas
§ Does best effort to place replicas in machines/racks to maximize availability
Alter table support
§ For adjusting number of replicas

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Updates in the RegionServer
Treats non-default replicas as read-only
Storefile management
§ Keeps itself up-to-date with the changes to do with store file creation/deletions

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IPC layer high level flow
Client
YES
Response within
timeout (10 millis)?
NO Send READ to all
secondaries
Send READ to primary
Poll for response
Wait for response
Take the first
successful response;
cancel others
Similar flow for GET/Batch-GET/
Scan, except that Scan is sticky
to the server it sees success
from.

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Performance and Testing
No significant performance issues discovered
§ Added interrupt handling in the RPCs to cancel unneeded replica RPCs
Deeper level of performance testing work is still in progress
Tested via IT tests
§ fails if response is not received within a certain time

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Next steps
What has been described so far is in “Phase-1” of the project
Phase-2
§ WAL replication
§ Handling of Merges and Splits
§ Latency guarantees
– Cancellation of RPCs server side
– Promotion of one Secondary to Primary, and recruiting a new Secondary
Use the infrastructure to implement consensus protocols for read/write
within a single datacenter

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Data Replication
Data should be replicated from primary regions to secondary regions
A regions data = Data files on hdfs + in-memory data in Memstores
Data files MUST be shared. We do not want to store multiple copies
Do not cause more writes than necessary
Two solutions:
§ Region snapshots : Share only data files
§ Async WAL Replication : Share data files, every region replica has its own in-memory data

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Data Replication – Region Snapshots
Primary region works as usual
§ Buffer up mutations in memstore
§ Flush to disk when full
§ Compact files when needed
§ Deleted files are kept in archive directory for some time
Secondary regions periodically look for new files in primary region
§ When a new flushed file is seen, just open it and start serving data from there
§ When a compaction is seen, open new file, close the files that are gone
§ Good for read-only, bulk load data or less frequently updated data
Implemented in phase 1

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Data Replication - Async WAL Replication
Being implemented in Phase 2
Uses replication source to tail the WAL files from RS
§ Plugs in a custom replication sink to replay the edits on the secondaries
§ Flush and Compaction events are written to WAL. Secondaries pick new files when they see
the entry
A secondary region open will:
§ Open region files of the primary region
§ Setup a replication queue based on last seen seqId
§ Accumulate edits in memstore (memory management issues in the next slide)
§ Mimic flushes and compactions from primary region

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Memory management & flushes
Memory Snapshots-based approach
§ The secondaries looks for WAL-edit entries Start-Flush, Commit-Flush
§ They mimic what the primary does in terms of taking snapshots
– When a flush is successful, the snapshot is let go
§ If the RegionServer hosting secondary is under memory pressure
– Make some other primary region flush
Flush-based approach
§ Treat the secondary regions as regular regions
§ Allow them to flush as usual
§ Flush to the local disk, and clean them up periodically or on certain events
– Treat them as a normal store file for serving reads

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Summary
Pros
§ High-availability for read-only tables
§ High-availability for stale reads
§ Very low-latency for the above
Cons
§ Increased memory from memstores of the secondaries
§ Increased blockcache usage
§ Extra network traffic for the replica calls
§ Increased number of regions to manage in the cluster

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References
Apache branch hbase-10070 (https://github.com/apache/hbase/tree/
hbase-10070)
HDP-2.1 comes with experimental support for Phase-1
More on the use cases for this work is in Sudarshan’s (Bloomberg) talk
§ “Case Studies” track titled “HBase at Bloomberg: High Availability Needs for the Financial
Industry”

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Thanks
Q & A