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. COMAD 2008, IIT Bombay 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

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

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

5. COMAD 2008, IIT Bombay 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

6. COMAD 2008, IIT Bombay Asynchronous IO Interface aio_read ( aio structure)Request an AIO read operation aio_error ( aio structure )Check the status of an AIO request lio_listio ( array of aio structures ) Initiate a list of AIO operations We use list AIO in our implementation Can initiate multiple IO read operations in one system call ( File descriptor, offset, buffer, numBytes, … ) Linux 2.6 kernel

7. COMAD 2008, IIT Bombay 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

8. COMAD 2008, IIT Bombay Demand-Driven Iterator  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. NL J sca n Table ATable B Open() Next() Close() Blocking call ! sca n

9. COMAD 2008, IIT Bombay  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 Agenda

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

11. COMAD 2008, IIT Bombay 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

12. COMAD 2008, IIT Bombay 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

13. COMAD 2008, IIT Bombay 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

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

15. COMAD 2008, IIT Bombay 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

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

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

18. COMAD 2008, IIT Bombay 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

19. COMAD 2008, IIT Bombay Async. versions of other operators Merge Join sort Seq scan T1T2 I can start the sorting of other input ! LATER Initiate AIO read

20. COMAD 2008, IIT Bombay 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

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

22. COMAD 2008, IIT Bombay 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 1 GB TPCH 10 GB

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

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

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

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

27. COMAD 2008, IIT Bombay Performance Results TPC-H Q12:select l_shipmode,sum(...) from orders,lineitem where o_orderkey = l_orderkey and group by l_shipmode order by l_shipmode Original INLAsync INLGain TPCH 1GB 1GB RAM 64.7 sec48 sec25 % TPCH 10 GB 1GB RAM 687 sec431 sec37 % TPCD 10GB RAID 10 4 disks, 3.2 GB RAM 164 sec147 sec10 %

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

29. COMAD 2008, IIT Bombay 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

30. COMAD 2008, IIT Bombay 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

31. COMAD 2008, IIT Bombay Thank You Questions ?

32. COMAD 2008, IIT Bombay 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

33. COMAD 2008, IIT Bombay 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