Postgres-XC: Symmetric PostgreSQL Cluster

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This presentation explains Postgres-XC architecture and its use case. It also explains the HA aspects of Postgres-XC and benchmarking results.

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Postgres-XC: Symmetric PostgreSQL Cluster

  1. 1. Postgres-XC Symmetric PostgreSQL Cluster Pavan Deolasee July 29, 2013 PostgreSQL Global Development Group Postgres-XC Global Development Group
  2. 2. Who am I ?  Pavan Deolasee Http://www.linkedin.com/in/pavandeolasee  Contributor to PostgreSQL and Postgres-XC Global Development Best known for development of Heap-Only-Tuple (HOT) feature of PostgreSQL 8.3 release Contributed several other enhancements to PostgreSQL and derivatives Associated with Postgres-XC since its inception Contributed to Postgres-XC's architectural design and implementation of several features  Previously worked for EnterpriseDB and Symantec/Veritas 11/21/13 Postgres-XC 2
  3. 3. Agenda  PostgreSQL  History of Postgres-XC  Architecture Overview  Data Distribution Strategies  Scalability and Performance Results  SPOF Analysis  Development Practices, Project Status and Roadmap  Support 11/21/13 Postgres-XC 3
  4. 4. PostgreSQL
  5. 5. PostgreSQL Recap  PostgreSQL is world's most advanced open source database is very stable is fully compliant with ANSI SQL supports foreign key, check constraints supports various kinds of indexes supports inheritance is fully extensible (data types, procedural languages etc) supports ACID transactions will recover your database in case of server failure 11/21/13 Postgres-XC 5
  6. 6. PostgreSQL Recap  PostgreSQL uses write-ahead-logs for durability and recover your database in case of server failure built-in log based streaming synchronous/asynchronous replication file system level backups and archive recovery point-in-time recovery hot standby binary upgrades full-text search and many more 11/21/13 Postgres-XC 6
  7. 7. Performance and Scalability   While PostgreSQL was becoming feature rich, scalability was still a concern, especially on high end machines. Significant work in last few releases to address performance and scalability issues Heap-only-tuples (HOT) in 8.3 for reuse of dead space Virtual transaction identifiers for read-only transactions Spread checkpoints to reduce spikes in disk IO Hot standby for read scalability Visibility maps, Index-only-scans Improved spin-lock implementations Linear read scalability for multi-core machines (64 cores) Unlogged tables 11/21/13 Postgres-XC 7
  8. 8. Performance and Scalability  Scale up is a continuous process Every major release comes up with new improvements Reduced contention for critical sections is important for multiprocess scalability  64 and 128 core machines are becoming common  Several hundred concurrent connections is a norm Efficient and full use of resources on high end machines     Large amounts of RAM While scale up is nice and very important, it may not be able to keep pace with the volume and growth of data Streaming replication and Hot standby allows read scalability, but each server should still be capable of handling full data Partitioning using inheritance and constraint exclusion 11/21/13 Postgres-XC 8
  9. 9. Performance and Scalability  When data grows rapidly, it may not be possible or affordable to upgrade hardware quickly  Scale out is a very attractive solution for BIG data use cases  PostgreSQL had some support as contrib module DBlink  Foreign Data Wrappers (FDW) Create foreign servers and foreign tables Natively query those tables from PostgreSQL server FDWs exist for Postgres, MySQL, File, MongoDB etc Though attractive, still in very early stage and may not scale with complex queries 11/21/13 Postgres-XC 9
  10. 10. History of Postgres-XC
  11. 11. Project Kick-off  2009 - NTT Data Intellilink Corporation, Japan and EnterpriseDB (my previous employer) decide to collaborate for developing a clustering solution for PostgreSQL NTT had previously internally tried to build another Postgres cluster (RITA-DB), but could not take it beyond prototype stage. Lessons learnt EnterpriseDB had previously acquired GridSQL, a scale-out solution. But it had its own issues including performance problems No other existing clustering solution was good enough   Slony-II  Pgpool-II  GridSQL  Log Shipping Standby  11/21/13 Postgres-R pl/proxy Postgres-XC 11
  12. 12. Project Mandate   Provide a viable open source, free alternative to a popular shared disk cluster from a very reputable commercial vendor. Yes, Oracle RAC Provide write-scalability At least 3.5 times scalability with 5 servers in a write intensive workload  Provide read-scalability Efficient utilization of cluster nodes Parallel execution 11/21/13 Postgres-XC 12
  13. 13. Project Mandate (Cont)  Provide global consistency Data written from one node must be available from other nodes immediately and in a consistent manner   Provide ACID properties of transaction Provide seamless API so that the client applications need no change or very minor change Same SQL Same libpq, JDBC, ODBC APIs  Must run and scale on commodity hardware Expensive shared storage Expensive network interconnects 11/21/13 Postgres-XC 13
  14. 14. Initial Team  Koichi Suzuki Chief architect and leader of the project, NTT Data Intellilink  Mason Sharp StormDB (previously at EnterpriseDB)  Pavan Deolasee (me) (previously at EnterpriseDB)  Many more from NTT, EnterpriseDB and open source community has joined and contributed since then (Part of the content is attributed to this team and the global development team) 11/21/13 Postgres-XC 14
  15. 15. Postgres-XC Architecture
  16. 16. So What is Postgres-XC ?  Shared nothing architecture  Symmetric PostgreSQL cluster No master/slave replication No read-only clusters Every node can issue both read/write Every node provides single consistent database view Transparent transaction management  Not just a replication Each table can be replicated/distributed by sharding Parallel transaction/query execution  So both read/write scalability 11/21/13 Postgres-XC 16
  17. 17. PostgreSQL Streaming Replication  Master-slave architecture  Write-Ahead-Log (WAL) based replication  Synchronous, asynchronous, cascading replication supported  Hot Standby Can answer read-only queries Standby may give a stale view of the database since the WAL application happens asynchronously Do not support table level replication 11/21/13 Postgres-XC 17
  18. 18. Postgres-XC: Symmetric Cluster Global Transaction Manager (GTM) Postgres-XC Servers 11/21/13 Postgres-XC 18
  19. 19. Postgres-XC Key Points  Postgres-XC is symmetric, shared nothing, scalable PostgreSQL cluster  Write Scalability  Read Scalability  Global Consistency 11/21/13 Postgres-XC 19
  20. 20. Postgres-XC Major Components Applications SQL / libpq / ODBC / JDBC Interface Coordinators SQL/libpq Interface GTM Datanodes 11/21/13 Postgres-XC 20
  21. 21. PostgreSQL MVCC   PostgreSQL uses Multi-version concurrency control (MVCC) for guaranteeing Isolation and Consistency (“CI” of ACID) MVCC increases concurrency readers don't wait on other readers/writers writers don't wait on readers    Every write transaction is assigned an XID (32-bit identifier) Every row that gets inserted/deleted/updated gets a stamp of the transaction that did the operation A MVCC Snapshot is used to decide if a given row or a version of a given row is visible to a transaction or not 11/21/13 Postgres-XC 21
  22. 22. PostgreSQL MVCC  Two important concepts Transaction Identifiers (XIDs) MVCC Snapshots  A consistent view of the database can be obtained by ensuring that these two values are managed properly 11/21/13 Postgres-XC 22
  23. 23. Global Transaction Manager (GTM)     The only central component in the XC cluster Provides globally consistent and visible transaction identifiers (GXID) to transactions running across the cluster Provides global snapshots to run queries across the cluster so that they see a consistent view irrespective of how many different nodes the query need to fetch data from GTM is not a database itself, though it keeps some simple state information  Its a separate binary supplied with Postgres-XC source  Runs as a independent server process 11/21/13 Postgres-XC 23
  24. 24. Global Transaction Manager (GTM)      There can only be ONE GTM per Postgres-XC cluster, though additional GTM standby can be configured (more later) Comes with a GTM-proxy (more later) for request pooling and scalability Multi-threaded process (one thread per connection) Recommended to run GTM on a separate physical machine for performance and scalability Helps in a few other side issues such as to manage sequences 11/21/13 Postgres-XC 24
  25. 25. Coordinator   Coordinator is the brain of Postgres-XC cluster Applications connect to the coordinators and issue SQL queries (they never talk to datanodes directly) Parse, plan and execute queries Knows about table distribution properties and takes advantage of the same for query planning Pushes down as much computation as possible to the datanodes Pushes down transaction identifiers and snapshots to other nodes Manages implicit two-phase transactions 11/21/13 Postgres-XC 25
  26. 26. Coordinator    Non-critical component of the Postgres-XC cluster There could be (and most likely there will be) more than one coordinator in the cluster Applications can connect to any coordinator and issue read/write queries No master coordinator  Modified version of PostgreSQL Remember PostgreSQL comes with PostgreSQL license 11/21/13 Postgres-XC 26
  27. 27. Datanode  The real guy managing all the data  Accepts requests from coordinators and sends responses back SQL/DML queries DDL queries Maintenance commands (VACUUM, CHECKPOINT, CLUSTER etc)  Modified PostgreSQL engine but with very minimal changes Transaction management, MVCC Sequences  Critical component of the cluster Failure will lead to DML/DDL queries failing unless the coordinator can decide to bypass the datanode because its not required for the given query 11/21/13 Postgres-XC 27
  28. 28. Data Distribution
  29. 29. Distribution Strategies  Distributed tables Each row exists only on a single datanode Distribution strategies   MODULO  ROUND ROBIN  User defined functions (Future)  Range (Future)   HASH Values (Future) Replicated tables Each row of the table is stored on all datanodes where the table is replicated 11/21/13 Postgres-XC 29
  30. 30. Distributed Tables  Write to a single row is applied only on the node where the row resides Multiple rows can be written in parallel Scanning rows spanning across the nodes (e.g. table scans) can hamper performance   Point reads and writes based on the distribution column value show good performance Datanode where the operation happens can be identified by the distribution column value in the query 11/21/13 Postgres-XC 30
  31. 31. Replicated Tables  Statement level replication Each write needs to be replicated Writes are costly  Read can happen on any node (where table is replicated) Reads from different coordinators can be routed to different nodes  Useful for relatively static tables, with high read load 11/21/13 Postgres-XC 31
  32. 32. Distribution Samples – Horizontal Scalability CREATE TABLE emp (empno integer, ename text) DISTRIBUTE BY HASH(empno); CREATE TABLE emp (empno integer, ename text) DISTRIBUTE BY MODULO(empno); CREATE TABLE emp (empno integer, ename text) DISTRIBUTE BY ROUND ROBIN; CREATE TABLE emp (empno integer, ename text);  Default is to distribute by HASH if the datatype is supported first column of primary key or first column of foreign key or first column of the table If everything else fails, distribute by ROUND ROBIN 11/21/13 Postgres-XC 32
  33. 33. Distribution Samples (Cont) CREATE TABLE emp (empno integer, ename text) DISTRIBUTE BY HASH(empno) TO NODE (N1, N2);   Distribute to all nodes or a set of nodes, either explicitly named or grouped together as named group Default is to distribute to all the nodes configured 11/21/13 Postgres-XC 33
  34. 34. Distributed Query Processing  Coordinator Accepts queries and plans them Finds the right data-nodes from where to fetch the data Frames queries to be sent to these data-nodes Gathers data from data-nodes Processes it to get the desired result  Datanode Executes queries from coordinator like PostgreSQL Has same capabilities as PostgreSQL 11/21/13 Postgres-XC 34
  35. 35. Distributed Query Processing   Query shipping Coordinator tries to delegate maximum query processing to datanodes Indexes are located on datanodes Materialization of huge results is avoided in case of sorting, aggregation, grouping, JOINs etc. Limit, Offset clauses are shipped to the datanodes ORDER BY, GROUP BY clauses are shipped to the datanodes Coordinator is freed to handle large number of connections Joins are shipped too  Distributing data wisely helps coordinator to delegate maximum query processing and improve performance 11/21/13 Postgres-XC 35
  36. 36. Scalability by Data Distribution
  37. 37. Write Scalability  Tables are distributed/sharded – usually HASH of the column value Horizontal partitioning  Inserts are directed to the datanode based on the value of the distributed column Integrated in the query executor Not trigger or rule based Supports COPY (bulk load mechanism of PostgreSQL)  Updates and Deletes also directed to the datanodes Coordinator is completely bypassed if the query does not have any local/cross dependency Highly scalable in many cases  Some queries must go through the coordinator because of dependencies 11/21/13 Postgres-XC 37
  38. 38. Write Scalability (Cont) CREATE TABLE test (a int, b text) DISTRIBUTE BY HASH(a); INSERT INTO test SELECT generate_series(1,100), 'foo'; EXPLAIN VERBOSE UPDATE test SET b = 'bar' WHERE a > 1; QUERY PLAN ---------------------------------------------------------------------------Data Node Scan on "__REMOTE_FQS_QUERY__" (cost=0.00..0.00 rows=0 width=0) Output: ('bar'::text), ('bar'::text), test.ctid, xc_node_id Node/s: node_dn1, node_dn2 Remote query: UPDATE public.test SET b = 'bar'::text WHERE (a > 1) (4 rows) 11/21/13 Postgres-XC 38
  39. 39. Write Scalability (Cont) EXPLAIN VERBOSE DELETE FROM test WHERE a = 100; QUERY PLAN ---------------------------------------------------------------------------Data Node Scan on "__REMOTE_FQS_QUERY__" (cost=0.00..0.00 rows=0 width=0) Output: test.a, test.ctid, xc_node_id Node/s: node_dn2 Remote query: DELETE FROM public.test WHERE (a = 100) (4 rows)  Queries are started in parallel on multiple nodes thus improving scalability of write operations 11/21/13 Postgres-XC 39
  40. 40. Read Scalability  Significant planner enhancements to improve read scalability Many cool features already in Continuous development for improving it further  Query is analyzed and shipped to a datanode as it is if there are no dependencies on the coordinator or other datanodes Fast Query Shipping  If entire query can not be shipped, parts of the query are shipped Offload as much processing as possible to the datanodes Do the final processing of data on the coordinator Reduce as much data movement from datanodes to the coordinator 11/21/13 Postgres-XC 40
  41. 41. Read Scalability (Cont)  Parallel query execution Query is stared in parallel on all involved datanodes Significantly improves performance by utilizing capacities across the cluster  Qualifications push-down WHERE clauses in a SELECT query are pushed down to the datanodes to reduce the selectivity of the result set Significantly reduces the amount of data fetched at the coordinator for final execution  Remote expressions push-down Push function and expression evaluation to the datanodes 11/21/13 Postgres-XC 41
  42. 42. Read Scalability (Cont)  Aggregates Enhanced PostgreSQL's two step aggregation to three step aggregation Collect summary at each datanode and then do the final aggregation at the coordinator An example, avg(integer) Datanode D1 - 1, 2, 5, 7 Datanode D2 - 3, 4, 6, 8, 9 Datanode D1 sends: sum(integer), count => 15, 4 Datanode D2 sends: sum(integer), count => 30, 5 Coordinator computes the final avg as (15 + 30) / (4 + 5) = 5 11/21/13 Postgres-XC 42
  43. 43. Read Scalability (Cont)  Join Push-down Complete or part of complex joins can be pushed down to the datanodes    Complex analysis of the tables involved in the join and their distribution strategy is required to arrive at a conclusion regarding what can be pushed and what must not be pushed Significant performance and scalability boost Order By Result sets are sorted on individual datanodes and fetched on the coordinator Coordinator does a final merge step of the sorted sets  Limit/Offset 11/21/13 Postgres-XC 43
  44. 44. Thumb Rules - Read-Write Queries  High point reads (based on distribution column) Distributed or replicated  High read activities but no frequent writes Better be replicated  High point writes Better be distributed  High insert-load, but no frequent update/delete/read Better be round-robin 11/21/13 Postgres-XC 44
  45. 45. Query Analysis  Find the relations/columns participating in equi-join conditions, WHERE clause etc. Distribute on those columns  Find columns participating in GROUP BY, DISTINCT clauses Distribute on those columns  Find columns/tables which are part of primary key and foreign key constraints Global constraints are not yet supported in XC Distribute on those columns 11/21/13 Postgres-XC 45
  46. 46. Example DBT1 11/21/13 Postgres-XC 46
  47. 47. Example DBT1  author, item Less frequently written Frequently read from Author and item are frequently JOINed  Dimension tables Hence replicated on all nodes 11/21/13 Postgres-XC 47
  48. 48. Example DBT1  customer, address, orders, order_line, cc_xacts Frequently written  hence distributed Participate in JOINs amongst each other with customer_id as JOIN key point SELECTs based on customer_id  hence distributed by hash on customer_id so that JOINs are shippable Participate in JOINs with item  11/21/13 Having item replicated helps pushing JOINs to datanodes Postgres-XC 48
  49. 49. Example DBT1  Shopping_cart, shopping_cart_line Frequently written  Hence distributed Point selects based on column shopping_cart_id  Hence distributed by hash on shopping_cart_id JOINs with item  11/21/13 Having item replicated helps Postgres-XC 49
  50. 50. Results – DBT1 11/21/13 Postgres-XC 50
  51. 51. Other Scalability Considerations
  52. 52. GTM Scalability  GTM can become a bottleneck serve XID for every new transaction in the cluster MVCC snapshot for transaction or every statement in a transaction (readcommitted transactions) Snapshot size can increases proportional to the number of concurrent transactions in the system   GTM has been heavily optimized and runs a multi-threaded process for scalability For even greater scalability, advised to run GTM Proxy on every coordinator 11/21/13 Postgres-XC 52
  53. 53. GTM Scalability – GTM Proxy  Runs as a proxy to GTM on other servers – preferably on the same server where coordinators run One proxy per coordinator server  Same API as GTM Components can connect to a proxy or GTM directly  Pools multiple GET TXID requests into a single request e.g get 100 new identifiers in a single request/response  Pools multiple GET SNAPSHOT requests into a single request Some manipulation of the received snapshot is required, but that's possible  Further pools requests of multiple types Reduces load on the GTM significantly One proxy per server scales near linearly 11/21/13 Postgres-XC 53
  54. 54. Coordinator Scalability  Coordinator does not store table data IO scalability not an issue  Query parsing, planning and part or full execution CPU utilization could be high  Add more coordinators if you need more computing power  Beware of connections explosion on the datanodes Every additional coordinator will need more connections to datanode Built-in connection pooler in the coordinator can help a lot 11/21/13 Postgres-XC 54
  55. 55. Datanode Scalability  Datanode stores and fetches table data from the disks  In case of IO saturation Add more datanodes and reconfigure existing tables  Easier said than done  May need data redistribution (except for round-robin distribution)  Table remains locked out (until concurrent data redistribution is added) Add more datanodes for new tables   11/21/13 Create new tables on the new datanode Existing tables are not impacted Postgres-XC 55
  56. 56. Transaction Management  Postgres-XC uses two-phase commit protocol for distributed transaction management Every commit will make two round-trips to the datanode (PREPARE and COMMIT) Optimized so that single phase commit is used if only one node is involved in a transaction Optimized so that only write-operating datanodes/coordinators are counted for deciding if 2PC is required or not  If possible, design transactions so that they span a single or as few nodes as possible 11/21/13 Postgres-XC 56
  57. 57. SFOF Analysis and Failure Handling
  58. 58. Single Point of Failure  GTM Obviously SPOF  GTM-Proxy No persistent data Just restart when fails  Coordinator Every coordinator is essentially a copy When fails, other coordinators work  Datanode SPOF for sharded table 11/21/13 Postgres-XC 58
  59. 59. SPOF - GTM  GTM keeps crucial information about next transaction identifier, currently running transactions as well as sequence information Actual fate of each transaction is not permanently saved on the GTM though (at least not today)  GTM failure leads to complete failure of the cluster any useful work would needs GTM interaction  GTM Standby for rescue 11/21/13 Postgres-XC 59
  60. 60. GTM Standby  GTM Standby is a working replica of the GTM Every operation on the GTM is replicated on the GTM, either via proxy or GTM  Create and configure standby Only one standby can be configured currently Can be added to a running cluster without any disruption Can be promoted to be the new master Same executable as GTM, only configuration change 11/21/13 Postgres-XC 60
  61. 61. SPOF - Datanode  Almost all the techniques for PostgreSQL HA are available Streaming replication Shared disk remount  Coordinators may require reconfiguration Coordinators should use the new datanode Coordinators should clean connections to failed datanode before reconfiguration  GTM Reconnect to (new) local GTM proxy 11/21/13 Postgres-XC 61
  62. 62. SPOF – Coordinator  Coordinator only maintains catalog information Stable and mostly read-only  All coordinators are replica of each other No master-slave concept. Every coordinator is equal  Datanode HA techniques can be applied Streaming replication Shared disk remount  Applications can continue to use other coordinators while the failed coordinator is restored 11/21/13 Postgres-XC 62
  63. 63. Development Process and Status
  64. 64. Development Process  Project hosted on sourceforge http://postgres-xc.sourceforge.net/  Join developer/user mailing lists postgres-xc-developers@lists.sourceforge.net postgres-xc-general@lists.sourceforge.net  Test and report bugs postgres-xc-bugs@lists.sourceforge.net  Submit patches 11/21/13 Postgres-XC 64
  65. 65. Regression and Performance Tests  PostgreSQL's regression tests are enhanced New tests added to test Postgres-XC specific features Existing tests modified to adjust for difference because of cluster environment   Nightly regression tests are run on buildfarm (at least last I checked) Regular performance tests are conducted by NTT Data to guard against performance regression Typically DBT1 tests Last I heard, DBT2 tests were being developed by NTT 11/21/13 Postgres-XC 65
  66. 66. Status  Release 1.1 is in beta stage (recommended) Based on latest PostgreSQL 9.2 stable release Many new features, enhancements and bug fixes   Triggers  Planner enhancements  GTM standby  Where current of   Data redistribution Returning Release 1.0 is the latest stable release Based on PostgreSQL 9.1 11/21/13 Postgres-XC 66
  67. 67. Release Management  Yearly major release Merge with PostgreSQL's new release Additional XC features  Quarterly minor release Bug fixes Security fixes Catch up with PostgreSQL's minor release 11/21/13 Postgres-XC 67
  68. 68. Licensing  Started with GPL license, but changed to PostgreSQL license couple of years back Anyone can take the source, modify and use without requiring to open source the modified code Very flexible licensing 11/21/13 Postgres-XC 68
  69. 69. Support  Very good support available from the core team on the mailing lists Helps if a self containing test case is included     Encouraged to fix bugs on your own and submit a patch A committer will look at the patch and commit (with changes if required) Volume of bug reports is still low and hence good turnaround time A few commercial companies have started offering professional support Good for project in the long run Skills of support staff could be limited right now 11/21/13 Postgres-XC 69
  70. 70. Limitations and Roadmap
  71. 71. Current Limitations  Multi-column distribution is not supported  Global constraints not supported Unique index on non-distribution column is not supported  Updating distribution column not supported  Statement level triggers not supported on the datanode side Statement level triggers will fire at the coordinator 11/21/13 Postgres-XC 71
  72. 72. Current Limitations  No cluster wide deadlock detection Timeout based deadlock resolution  No cost estimation at coordinator (table statistics not available/used at the coordinator) ANALYZE command is propagated to the datanodes and the stats are updated on the datanodes Datanodes have up-to-date statistics. So they will plan the queries in the most efficient way  Node management needs much improvement No built-in HA for replicated tables SELECT queries 11/21/13 Postgres-XC 72
  73. 73. Current Limitations  Truly serializable transactions not supported Read-committed and repeatable reads are supported  Transactions accessing TEMP objects can not be committed with 2PC PostgreSQL does not support PREPARing a transaction that has accessed temporary objects Set enforce_two_phase_commit = off to handle such transactions   Savepoints not supported Exception blocks in plpgsql not supported (they need savepoint support) 11/21/13 Postgres-XC 73
  74. 74. Roadmap   Support for remaining SQL features (continuous process) Planner enhancements so that more and complex queries can be pushed down to the datanodes  Table statistics at the coordinator  Concurrent redistribution of data after node additional/removal Without exclusive lock on the table  Separate group in NTT is developing resource agents for corosync/pacemaker Plans not yet in public domain  Installers and further testing with DBT-2/DBT-5 11/21/13 Postgres-XC 74
  75. 75. Resources
  76. 76. Project Resources  Postgres-XC project home page http://postgres-xc.sourceforge.net/ Project wiki and links to other presentations/white papers  Downloads and installation Product RPMS available for download  Source code Source code from GIT repository Zipped tar balls  Other tools pgxc_ctl utility to configure/manage/monitor the cluster A bunch of other contrib modules 11/21/13 Postgres-XC 76
  77. 77. Project Resources  Documentation Extensive Postgres style documentation explaining XC-specific features Available online as well as distributed with source/rpms.  Mailing lists Postgres-xc-general Postgres-xc-developers Postgres-xc-bugs  Write to me pavan.deolasee@gmail.com 11/21/13 Postgres-XC 77
  78. 78. Thank You

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