This document discusses high-throughput transactional stream processing on HBase using Continuuity Reactor. Continuuity Reactor is a platform built on Hadoop and HBase that allows collecting, processing, storing and querying data with ACID guarantees using a flow-based programming model. Flows in Reactor are composed of flowlets connected by queues, with all processing occurring within transactions for consistency. The document outlines how Reactor implements transactions using an optimistic concurrency control approach based on multi-version concurrency control and HBase timestamps. It also discusses queue design and optimizations for performance.

1. HIGH-THROUGHPUT TRANSACTIONAL
STREAM PROCESSING
ON HBASE
Alex Baranau @abaranau
Andreas Neumann @anew68
Thursday, June 6, 13

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WHO WE ARE
• We’ve built Continuuity Reactor: the world’s first scale-out
application server for Hadoop
• Fast, easy development, deployment and management of
Hadoop and HBase apps
• Continuuity team has years of experience in using and contributing
to Open Source, and we intend to continue doing so.
Thursday, June 6, 13

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AGENDA
• Transactions in stream processing: what? why?
• Omid-style transactions explained
• Queues: heart of stream processing
• What’s next?
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THE REACTOR
• Continuuity Reactor is an app platform built on Hadoop and HBase
• Collect, Process, Store, and Query data.
• A Flow is a real-time processor with exactly-once guarantee
• A flow is composed of flowlets, connected via queues
• All processing happens with ACID guarantees in transactions
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PROCESSING IN A FLOWLET
...Queue ...
...
Flowlet
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PROCESSING IN A FLOWLET
...Queue ...
...
Flowlet
DataSet
... ...
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TRANSACTIONS: WHY?
...Queue ...
...
Flowlet
DataSet
... ...
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PROCESSING WITH TX
...Queue ...
...
Flowlet
DataSet
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TRANSACTIONS: WHAT?
• Atomic - Entire transaction is committed as one
• Consistent - No partial state change due to failure
• Isolated - No dirty reads, transaction is only visible after commit
• Durable - Once committed, data is persisted reliably
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OMID-STYLE TRANSACTIONS
• Multi-Version Concurrency Control with Version = HBase Timestamp
• All writes in the same transaction use the transaction ID as timestamp
• Reads exclude other, uncommitted transactions (for isolation)
• Optimistic Concurrency Control
• Conflict detection at commit of transaction
• Write Conflict: two overlapping transactions write the same row
• Rollback of one transaction in case of conflict (whichever commits later)
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OMID-STYLE TRANSACTIONS
start tx
do work
has conflicts
commit tx
Tx Oracle
get
write pointer
HBase
write with
version=pointer
rollback
abort tx
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OPTIMISTIC CONCURRENCY CONTROL
• Optimistic Concurrency Control
• Avoids cost of locking rows and tables
• No deadlocks or lock escalations
• Cost of conflict detection and possible rollback is higher
• Good if conflicts are rare: short transaction, disjoint partitioning of
work
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OMID-STYLE TRANSACTIONS
has conflicts
create tx
track tx ops
check conflicts
make tx visible
commit tx
no conflicts
start tx
do work
remove txabort tx
get ops
to rollback
get new
tx
add ops
to tx
try
commit
Tx OracleTx Agent
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TRANSACTION ORACLE
• Simple & Fast
• Single point of failure?
• Persist all state to a write-ahead log
• Secondary oracle that subscribes to log
• Failover can happen quickly
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QUEUES
• Flowlets pass data to each other on queues
• Every consumer (flowlet) can be partitioned
• More than one consumer (flowlet) can read a queue
• Queues are partitioned to scale throughput
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Flowlet
QUEUES & FLOWLETS
...Queue ...
Instance2
Instance1
...
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QUEUE DESIGN
• Queue entries are written in sequence
• Write pointer only goes forward
• Queue entries are read sequentially
• Read pointer only goes forward
• Reader waits for entry to be written
• Transactions are used for isolation & consistency guarantees
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QUEUE OPERATION
WritePointer
...
enqueue
inc & get entry meta valid?
... ... ...
[data] [data] trueenqueue
Queue
set falsecommit tx abort tx
start tx
write
commit tx abort tx
start tx ReadPointer
Consumer State
Claimed
Entries List
inc & get
read
...
Producer Consumer
put
dequeue
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PERFORMANCE CONSIDERATIONS
• Every entry costs at least 4 writes:
• Enqueue = Increment + Put = 2 writes to WAL
• Dequeue = 2 x Get + Put = 1 write to WAL
• Ack = Get + Put = 1 write to WAL
• Caching consumer state in-memory (still persisting every change)
• Dequeue = Get + Put
• Ack = Put
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PERFORMANCE IMPROVEMENTS
• Prefetching n entries and caching them in state:
• Dequeue = 1/n x Put
• Batch enqueues + dequeues
• Enqueue = 2/n x Put
• Dequeue = 1/n x Get + 1/n Put
• Ack = 1/n x Put
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PERFORMANCE NUMBERS
• enqueue: 10K ops/sec per producer per node
• dequeue: 5K ops/sec per consumer per node
• 2 vs 1 RPC calls comparing to enqueue op
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HBASE WISHLIST
• Filters for Get, not just max timestamp (for transactional read)
• Filters for Increment and CheckAndPut (for transactional writes)
• Ability to aggregate writes to WAL in co-processors (for faster queues)
• No-read atomic Append operation
• No-read atomic Increment operation
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QS?
Looking for the chance to work with a team that is defining a new category within Big Data?
We are hiring!
careers@continuuity.com
Thursday, June 6, 13