Exploiting Asynchronous IO using the Asynchronous Iterator Model

1. Exploiting Asynchronous IO using the Asynchronous Iterator Model Suresh Iyengar * S. Sudarshan Santosh Kumar # Raja Agrawal &IIT Bombay Current affiliations: * Microsoft Hyderabad, # Guruji.com, & SAP

2. Agenda • AIO Background • Exploiting AIO in query processing • Asynchronous Iterator model • Asynchronous Index Nested Loops Join • Asynchronous versions of other operators • Performance results • Related Work • Conclusion COMAD 2008 , IIT Bombay

3. IO Processing : Traditional way Application Kernel System call Read () Initiate IO Context switch Read response data Application Blocked ! • CPU is idle most of the time waiting for an IO completion COMAD 2008 , IIT Bombay

4. IO Processing : Async. way Application Kernel System call Initiate IO AIO Read () Notify Read response Do other work !! data COMAD 2008 , IIT Bombay

5. IO Processing : Async. Way • Asynchronous approach • Overlap of CPU and IO processing • Application can generate multiple IO requests • Allows IO subsystem to reorder access to data on disk • Important in RAID environments COMAD 2008 , IIT Bombay

6. Asynchronous IO Interface ( File descriptor, offset, buffer, numBytes, … ) Linux 2.6 kernel • We use list AIO in our implementation • Can initiate multiple IO read operations in one system call COMAD 2008 , IIT Bombay

7. Handling AIO completion • Signal-based handler • A signal is generated on IO completion • Callback using interrupts • An interrupt is generated on IO completion • Concurrent access to completion handler and shared data structures in both of above methods • Polling • Store IO requests in pending queue and poll periodically for completion • Our experiments show polling beats signal/interrupt based approach Call completion handler COMAD 2008 , IIT Bombay

8. Demand-Driven Iterator • Open() • Next() • Close() • Bottom level nodes perform operations such as sequential scans or index scans. • Upper level nodes are join nodes or other operator nodes such as sort or aggregate. NLJ scan scan Blocking call ! Table A Table B COMAD 2008 , IIT Bombay

9. Agenda • AIO Background • Exploiting AIO in query processing • Asynchronous Iterator model • Asynchronous Index Nested Loop (INL) Joins • Asynchronous versions of other operators • Performance results • Related Work • Conclusion COMAD 2008 , IIT Bombay

10. Asynchronous Iterator • Open() • Next() • Close() NLJ scan scan • I don’t have the tuple available in the memory !! • Issue AIO read operation • Return “LATER” Table A Table B Non- Blocking call ! COMAD 2008 , IIT Bombay

11. Asynchronous Iterator Model (AIM) • Allow a node to return a status “LATER” to the parent • Instead of blocking for IO completion. • The parent operator could • Perform other work, such as fetching data from another input • Simply return a LATER status to its parent node • Or just loop, reinvoking the child operator till it returns a tuple • E.g. root of the execution plan tree • Exact action depends on operator • Asynchronous versions of different operators • Focus on Asynchronous Indexed Nested Loops join COMAD 2008 , IIT Bombay

12. Asynchronous INL Joins • Original state of Indexed Nested Loops (INL) node • Left and right subplans and qualifier lists • Augmented state for async INL node • An array of outer tuples each having a queue of matching inner TIDs • AIO may have been issued for some already, others later • A workqueue for outer slots which already have AIO issued for their matching inner TIDS • An IO queue recording all pending AIO requests made by the node • Used to poll for completion of AIO requests COMAD 2008 , IIT Bombay

13. Asynchronous INL Join (contd.) • We divide the async INL join operations into two stages • Stage 1: Fetch outer tuples and issues AIO requests • Stage 2: Check for AIO completion, process AIO results and return join results. • Stages are interleaved • Stage 1 may be in progress for some tuples, and Stage 2 for others COMAD 2008 , IIT Bombay

14. Asynchronous INL Join (contd.) Stage 1 Fetch outer tuples For each outer tuple Find the matching inner TIDs for each outer tuple Put the outer tuple in workqueue Issue LIST AIO for matching inner TIDS of all outer tuples in workqueue (subject to BATCH_SIZE) COMAD 2008 , IIT Bombay

15. Asynchronous INL Join (contd.) • Rules • Batch size • BATCH_SIZE: max number of outstanding AIO requests • Why? OS limits, efficiency issues • We set the MAX_BATCH_SIZE per node to 200 in our experiments • Scale BATCH_SIZE in powers of 2 till MAX_BATCH_SIZE so that async INL can output tuples quickly at the onset • Case where outer tuple matches a large number of inner tuples is handled appropriately • Keeping the AIO queue filled • We issue further AIO requests (fetching outer tuples as required) if 10 % of earlier AIO requests have completed COMAD 2008 , IIT Bombay

16. Asynchronous INL Join (contd.) For each outer tuple in workqueue Stage 2 Check if any matching inner TIDs are present in memory No Present ? Yes • Remove that inner TID from outer tuple’s TID array • Perform join and add to result • if join result found break from loop Update workqueue Next page .. COMAD 2008 , IIT Bombay

17. Asynchronous INL Join (contd.) Prev page.. Yes Return resultto parent node Any join results? Back to start of Stage 2 Yes No No Poll for AIO completion Is tuple found orparent node cannot handle LATER Is no outstanding outer tuples & reached end of outer tuple Yes No tupStat = END_OF_RESULT result = NULL tupstat = LATERresult = NULL Return result and tupStat to parent node COMAD 2008 , IIT Bombay

18. Async. versions of other operators • Async Sequential scan • Check if next tuple is in the in-memory buffer • If its present, return the tuple • Else initiate an async read. Set tupStat = LATER and return • Out of order sequential scan • Start returning the tuples of a particular relation which are already there in the memory • even if out of order • Concurrently, issue AIO for other tuples COMAD 2008 , IIT Bombay

19. Async. versions of other operators I can start the sorting of other input ! Merge Join LATER sort sort LATER Seq scan Seq scan T1 T2 Initiate AIO read COMAD 2008 , IIT Bombay

20. Performance Results • Experiments with TPC-H database with scale factors of 1 and 10 in three different setups • Core 2 duo P4 with: • 1GB RAM and TPC-H - 1 GB database (single disk) • 1GB RAM and TPC-H – 10 GB database (single disk) • 3.2GB RAM and TPC-H – 10 GB database (4 disks / RAID 10) • We use PostgreSQL 8.1.3 as the code base • Compare it with our modified version of the same code base, incorporating asynchronous iterator model • with async INL and async seq. scan COMAD 2008 , IIT Bombay

21. Performance Results: 1GB RAM Query 1a: select l_orderkey, l_quantity from orders, lineitem where o_orderkey=l_orderkey andl_orderkey%100=2and l_linestatus=’F’ TPCH 1 GB TPCH 10 GB COMAD 2008 , IIT Bombay

22. Performance Results: 1 GB RAM Query 2a:select l_orderkey,l_quantity from orders,lineitem,customer where o_orderkey=l_orderkey and o_custkey=c_custkey and l_orderkey%100=2 and l_linestatus=’F’ TPCH 10 GB TPCH 1 GB COMAD 2008 , IIT Bombay

23. Performance Results : 1GB RAM Query 2a : Join of orders, lineitem and customer with filter (TPCH 1GB ) Startup effect COMAD 2008 , IIT Bombay

24. Performance Results: 1 GB RAM Query 2b: select l_orderkey,l_quantity frommyorders,lineitem,customer where o_orderkey=l_orderkey and o_custkey=c_custkey -- No tight selection TPCH 1 GB TPCH 10 GB 1GB RAM COMAD 2008 , IIT Bombay

25. Performance Results: 3.2 GB + RAID Query 2a : Join of orders, lineitem and customer with filter Query 1a : Join of orders and lineitem with filter TPC-H 10GB / 3.2GB RAM / 4 disks RAID10 COMAD 2008 , IIT Bombay

26. Performance Results: 3.2 GB + RAID Query 1b : Join of myorders, lineitem Query 2b : Join of myorders, lineitem and customer TPC-H 10GB / 3.2GB RAM / 4 disks RAID10 COMAD 2008 , IIT Bombay

27. Performance Results TPC-H Q12:select l_shipmode,sum(...) from orders,lineitem where o_orderkey = l_orderkey and <several selection> group by l_shipmode order by l_shipmode COMAD 2008 , IIT Bombay

28. Related Work • Graefe’s generalized spool iterator (Graefe [ BTW03 ]) • Pre-fetches multiple outer tuples • Issue AIO for matching inner TIDS • Can be replenished when empty or when one tuple is joined INL Spool operator Index lookup scan COMAD 2008 , IIT Bombay

29. Related Work • AIO used in database products • Microsoft SQL Server, IBM DB2, Oracle • No public documentation on how these systems use AIO • Asynchronous iteration for evaluating web queries (R.Goldman and J. Widom [ SIGMOD 2000 ] ) • They report results only on web queries COMAD 2008 , IIT Bombay

30. Conclusion • Proposed the Asynchronous Iterator Model (AIM) • Presented asynchronous versions of INL and some operators • Showed gains of over 50 % in some cases • AIM can be useful in web-service access and in data integration systems like IBM DataJoiner • Future work • Implementing async versions for index lookup, sub plan, sort and merge operator • Performing async IO in the presence of ordering constraints COMAD 2008 , IIT Bombay

31. Thank YouQuestions ? COMAD 2008 , IIT Bombay

32. Plans • Query 1a : • Seq scan on lineitem, probe on orders • Merge Join -> Index Scan on orders -> Sort lineitem -> Seq Scan on lineitem • Query 2a: • Nested Loop -> Nested Loop -> Seq Scan on lineitem -> Index Scan on orders -> Index Scan on customer COMAD 2008 , IIT Bombay

33. Plans • Query 2a Merge Join -> Sort orders -> Merge Join -> Index Scan on orders -> Sort on lineitem -> Seq Scan on lineitem -> Index Scan on customer • Query 2b : Nested Loop -> Nested Loop -> Seq Scan on lineitem -> Index Scan on myorders -> Index Scan on customer COMAD 2008 , IIT Bombay