Discuss building a multi-terabyte PostgreSQL instance in a high volume, mission-critical operational datastore that replaced Oracle. Learn about solving real-life problems such as a near-catastrophic hardware failure at terabyte level.

1. Big Bad PostgreSQL: A Case Study
Moving a
“large,”
“complicated,” and
mission-critical
datawarehouse
from Oracle
to PostgreSQL
for cost control.
1

2. About the Speaker
• Principal @ OmniTI
S32699X_Scalable_Internet.qxd 6/23/06 3:31 PM Page 1
Scalable Internet Architectures
• Open Source
Theo Schlossnagle
Scalable Internet Architectures
With an estimated one billion users worldwide, the Internet today is nothing less than a
global subculture with immense diversity, incredible size, and wide geographic reach. With a
relatively low barrier to entry, almost anyone can register a domain name today and potentially
provide services to people around the entire world tomorrow. But easy entry to web-based
commerce and services can be a double-edged sword. In such a market, it is typically much
harder to gauge interest in advance, and the negative impact of unexpected customer traffic
can turn out to be devastating for the unprepared.
mod_backhand, spreadlogd,
In Scalable Internet Architectures, renowned software engineer and architect Theo
Schlossnagle outlines the steps and processes organizations can follow to build online
services that can scale well with demand—both quickly and economically. By making
intelligent decisions throughout the evolution of an architecture, scalability can be a matter
Scalable Internet
of engineering rather than redesign, costly purchasing, or black magic.
OpenSSH+SecurID, Daiquiri,
Filled with numerous examples, anecdotes, and lessons gleaned from the author’s years
of experience building large-scale Internet services, Scalable Internet Architectures is both
thought-provoking and instructional. Readers are challenged to understand first, before they
Architectures
start a large project, how what they are building will be used, so that from the beginning
they can design for scalability those parts which need to scale. With the right approach, it
should take no more effort to design and implement a solution that scales than it takes
Wackamole, libjlog, Spread,
to build something that will not—and if this is the case, Schlossnagle writes, respect
yourself and build it right.
Theo Schlossnagle is a principal at OmniTI Computer Consulting, where he provides
expert consulting services related to scalable Internet architectures, database replication,
Reconnoiter, etc.
and email infrastructure. He is the creator of the Backhand Project and the Ecelerity MTA,
and spends most of his time solving the scalability problems that arise in high performance
and highly distributed systems.
Internet/Programming Cover image © Digital Vision/Getty Images
• Closed Source
Schlossnagle
Scalability
$49.99 USA / $61.99 CAN / £35.99 Net UK
Performance
Security
www.omniti.com
DEVELOPER’S
LIBRARY
DEVELOPER’S
www.developers-library.com LIBRARY
Message Systems MTA,
Message Central
• Author
Scalable Internet Architectures

3. Overall Architecture
OLTP instance:
Oracle 8i
drives the site
0.5 TB 0.25 TB
Hitachi JBOD
OLTP
Log import and Oracle 8i
processing
Oracle 8i
0.75 TB
JBOD
MySQL
log importer 0.5 TB 1.5 TB
Hitachi MTI OLTP warm backup
Warm spare
1.2 TB
SATA Datawarehouse
RAID
Log Importer
MySQL 4.1
1.2 TB
IDE RAID
Data Exporter
bulk selects / data exports

4. Overall Architecture
OLTP instance:
Oracle 8i
drives the site
0.5 TB 0.25 TB
Hitachi JBOD
OLTP
Log import and Oracle 8i
processing
Oracle 8i
0.75 TB
JBOD
MySQL
log importer 0.5 TB 1.5 TB
Hitachi MTI OLTP warm backup
Warm spare
1.2 TB
SATA Datawarehouse
RAID
Log Importer
MySQL 4.1
1.2 TB
IDE RAID
Data Exporter
bulk selects / data exports

5. Database Situation

6. Database Situation
• The problems:
• The database is growing.
• The OLTP and ODS/warehouse are too slow.
• A lot of application code against the OLTP system.
• Minimal application code against the ODS system.

7. Database Situation
• The problems:
• The database is growing.
• The OLTP and ODS/warehouse are too slow.
• A lot of application code against the OLTP system.
• Minimal application code against the ODS system.
• Oracle:
• Licensed per processor.
• Really, really, really expensive on a large scale.

8. Database Situation
• The problems:
• The database is growing.
• The OLTP and ODS/warehouse are too slow.
• A lot of application code against the OLTP system.
• Minimal application code against the ODS system.
• Oracle:
• Licensed per processor.
• Really, really, really expensive on a large scale.
• PostgreSQL:
• No licensing costs.
• Good support for complex queries.

9. Database Choices

10. Database Choices
• Must keep Oracle on OLTP
•Complex, Oracle-specific web application.
•Need more processors.

11. Database Choices
• Must keep Oracle on OLTP
• Complex, Oracle-specific web application.
• Need more processors.
• ODS: Oracle not required.
• Complex queries from limited sources.
• Needs more space and power.

12. Database Choices
• Must keep Oracle on OLTP
• Complex, Oracle-specific web application.
• Need more processors.
• ODS: Oracle not required.
• Complex queries from limited sources.
• Needs more space and power.
• Result:
• Move ODS Oracle licenses to OLTP
• Run PostgreSQL on ODS

13. PostgreSQL gotchas

14. PostgreSQL gotchas
• For an OLTP system that does thousands of
updates per second, vacuuming is a hassle.

15. PostgreSQL gotchas
• For an OLTP system that does thousands of
updates per second, vacuuming is a hassle.
• No upgrades?!

16. PostgreSQL gotchas
• For an OLTP system that does thousands of
updates per second, vacuuming is a hassle.
• No upgrades?!
• Less community experience with large
databases.

17. PostgreSQL gotchas
• For an OLTP system that does thousands of
updates per second, vacuuming is a hassle.
• No upgrades?!
• Less community experience with large
databases.
• Replication features less evolved.

18. PostgreSQL ♥ ODS

19. PostgreSQL ♥ ODS
• Mostly inserts.

20. PostgreSQL ♥ ODS
• Mostly inserts.
• Updates/Deletes controlled, not real-time.

21. PostgreSQL ♥ ODS
• Mostly inserts.
• Updates/Deletes controlled, not real-time.
• pl/perl (leverage DBI/DBD for remote
database connectivity).

22. PostgreSQL ♥ ODS
• Mostly inserts.
• Updates/Deletes controlled, not real-time.
• pl/perl (leverage DBI/DBD for remote
database connectivity).
• Monster queries.

23. PostgreSQL ♥ ODS
• Mostly inserts.
• Updates/Deletes controlled, not real-time.
• pl/perl (leverage DBI/DBD for remote
database connectivity).
• Monster queries.
• Extensible.

24. Choosing Linux

25. Choosing Linux
• Popular, liked, good community support.

26. Choosing Linux
• Popular, liked, good community support.
• Chronic problems:

27. Choosing Linux
• Popular, liked, good community support.
• Chronic problems:
• kernel panics

28. Choosing Linux
• Popular, liked, good community support.
• Chronic problems:
• kernel panics
• filesystems remounting read-only

29. Choosing Linux
• Popular, liked, good community support.
• Chronic problems:
• kernel panics
• filesystems remounting read-only
• filesystems don’t support snapshots

30. Choosing Linux
• Popular, liked, good community support.
• Chronic problems:
• kernel panics
• filesystems remounting read-only
• filesystems don’t support snapshots
• LVM is clunky on enterprise storage

31. Choosing Linux
• Popular, liked, good community support.
• Chronic problems:
• kernel panics
• filesystems remounting read-only
• filesystems don’t support snapshots
• LVM is clunky on enterprise storage
• 20 outages in 4 months

32. Choosing Solaris 10

33. Choosing Solaris 10
• Switched to Solaris 10

34. Choosing Solaris 10
• Switched to Solaris 10
• No crashes, better system-level tools.

35. Choosing Solaris 10
• Switched to Solaris 10
• No crashes, better system-level tools.
• prstat, iostat, vmstat, smf, fault-
management.

36. Choosing Solaris 10
• Switched to Solaris 10
• No crashes, better system-level tools.
• prstat, iostat, vmstat, smf, fault-
management.
• ZFS

37. Choosing Solaris 10
• Switched to Solaris 10
• No crashes, better system-level tools.
• prstat, iostat, vmstat, smf, fault-
management.
• ZFS
• snapshots (persistent), BLI backups.

38. Choosing Solaris 10
• Switched to Solaris 10
• No crashes, better system-level tools.
• prstat, iostat, vmstat, smf, fault-
management.
• ZFS
• snapshots (persistent), BLI backups.
• Excellent support for enterprise storage.

39. Choosing Solaris 10
• Switched to Solaris 10
• No crashes, better system-level tools.
• prstat, iostat, vmstat, smf, fault-
management.
• ZFS
• snapshots (persistent), BLI backups.
• Excellent support for enterprise storage.
• DTrace.

40. Choosing Solaris 10
• Switched to Solaris 10
• No crashes, better system-level tools.
• prstat, iostat, vmstat, smf, fault-
management.
• ZFS
• snapshots (persistent), BLI backups.
• Excellent support for enterprise storage.
• DTrace.
• Free (too).

41. Oracle features we need

42. Oracle features we need
• Partitioning

43. Oracle features we need
• Partitioning
• Statistics and Aggregations

44. Oracle features we need
• Partitioning
• Statistics and Aggregations
• rank over partition, lead, lag, etc.

45. Oracle features we need
• Partitioning
• Statistics and Aggregations
• rank over partition, lead, lag, etc.
• Large selects (100GB)

46. Oracle features we need
• Partitioning
• Statistics and Aggregations
• rank over partition, lead, lag, etc.
• Large selects (100GB)
• Autonomous transactions

47. Oracle features we need
• Partitioning
• Statistics and Aggregations
• rank over partition, lead, lag, etc.
• Large selects (100GB)
• Autonomous transactions
• Replication from Oracle (to Oracle)

48. Partitioning
For large data sets:

49. Partitioning
For large data sets:
pgods=# select count(1) from ods.ods_tblpick_super;

50. Partitioning
For large data sets:
pgods=# select count(1) from ods.ods_tblpick_super;
count
------------
1790994512
(1 row)

51. Partitioning
For large data sets:
pgods=# select count(1) from ods.ods_tblpick_super;
count
------------
1790994512
(1 row)
• Next biggest tables: 850m, 650m, 590m

52. Partitioning
For large data sets:
pgods=# select count(1) from ods.ods_tblpick_super;
count
------------
1790994512
(1 row)
• Next biggest tables: 850m, 650m, 590m
• Allows us to cluster data over specific ranges
(by date in our case)

53. Partitioning
For large data sets:
pgods=# select count(1) from ods.ods_tblpick_super;
count
------------
1790994512
(1 row)
• Next biggest tables: 850m, 650m, 590m
• Allows us to cluster data over specific ranges
(by date in our case)
• Simple, cheap archiving and removal of data.

54. Partitioning
For large data sets:
pgods=# select count(1) from ods.ods_tblpick_super;
count
------------
1790994512
(1 row)
• Next biggest tables: 850m, 650m, 590m
• Allows us to cluster data over specific ranges
(by date in our case)
• Simple, cheap archiving and removal of data.
• Can put ranges used less often in different
tablespaces (slower, cheaper storage)

55. Partitioning PostgreSQL style

56. Partitioning PostgreSQL style
• PostgreSQL doesn’t support partition...

57. Partitioning PostgreSQL style
• PostgreSQL doesn’t support partition...
• It supports inheritance... (what’s this?)

58. Partitioning PostgreSQL style
• PostgreSQL doesn’t support partition...
• It supports inheritance... (what’s this?)
• some crazy object-relation paradigm.

59. Partitioning PostgreSQL style
• PostgreSQL doesn’t support partition...
• It supports inheritance... (what’s this?)
• some crazy object-relation paradigm.
• We can use it to implement partitioning:

60. Partitioning PostgreSQL style
• PostgreSQL doesn’t support partition...
• It supports inheritance... (what’s this?)
• some crazy object-relation paradigm.
• We can use it to implement partitioning:
• One master table with no rows.

61. Partitioning PostgreSQL style
• PostgreSQL doesn’t support partition...
• It supports inheritance... (what’s this?)
• some crazy object-relation paradigm.
• We can use it to implement partitioning:
• One master table with no rows.
• Child tables that have our partition constraints.

62. Partitioning PostgreSQL style
• PostgreSQL doesn’t support partition...
• It supports inheritance... (what’s this?)
• some crazy object-relation paradigm.
• We can use it to implement partitioning:
• One master table with no rows.
• Child tables that have our partition constraints.
• Rules on the master table for insert/update/delete.

63. Partitioning PostgreSQL realized

64. Partitioning PostgreSQL realized
• Cheaply add new empty partitions

65. Partitioning PostgreSQL realized
• Cheaply add new empty partitions
• Cheaply remove old partitions

66. Partitioning PostgreSQL realized
• Cheaply add new empty partitions
• Cheaply remove old partitions
• Migrate less-often-accessed partitions to slower
storage

67. Partitioning PostgreSQL realized
• Cheaply add new empty partitions
• Cheaply remove old partitions
• Migrate less-often-accessed partitions to slower
storage
• Different indexes strategies per partition

68. Partitioning PostgreSQL realized
• Cheaply add new empty partitions
• Cheaply remove old partitions
• Migrate less-often-accessed partitions to slower
storage
• Different indexes strategies per partition
• PostgreSQL >8.1 supports constraint checking on
inherited tables.

69. Partitioning PostgreSQL realized
• Cheaply add new empty partitions
• Cheaply remove old partitions
• Migrate less-often-accessed partitions to slower
storage
• Different indexes strategies per partition
• PostgreSQL >8.1 supports constraint checking on
inherited tables.
• smarter planning

70. Partitioning PostgreSQL realized
• Cheaply add new empty partitions
• Cheaply remove old partitions
• Migrate less-often-accessed partitions to slower
storage
• Different indexes strategies per partition
• PostgreSQL >8.1 supports constraint checking on
inherited tables.
• smarter planning
• smarter executing

71. RANK OVER PARTITION
• In Oracle:
• In PostgreSQL:
With 8.4, we have windowing functions

72. RANK OVER PARTITION
• In Oracle:
select userid, email from (
select u.userid, u.email,
row_number() over
(partition by u.email order by userid desc) as position
from (...)) where position = 1
• In PostgreSQL:
With 8.4, we have windowing functions

73. RANK OVER PARTITION
• In Oracle:
select userid, email from (
select u.userid, u.email,
row_number() over
(partition by u.email order by userid desc) as position
from (...)) where position = 1
• In PostgreSQL:
FOR v_row IN select u.userid, u.email from (...) order by email, userid desc
LOOP
IF v_row.email != v_last_email THEN
RETURN NEXT v_row;
v_last_email := v_row.email;
v_rownum := v_rownum + 1;
END IF;
END LOOP;
With 8.4, we have windowing functions

74. Large SELECTs
• Application code does:

75. Large SELECTs
• Application code does:
select u.*, b.browser, m.lastmess
from ods.ods_users u,
ods.ods_browsers b,
( select userid, min(senddate) as senddate
from ods.ods_maillog
group by userid ) m,
ods.ods_maillog l
where u.userid = b.userid
and u.userid = m.userid
and u.userid = l.userid
and l.senddate = m.senddate;

76. Large SELECTs
• Application code does:
select u.*, b.browser, m.lastmess
from ods.ods_users u,
ods.ods_browsers b,
( select userid, min(senddate) as senddate
from ods.ods_maillog
group by userid ) m,
ods.ods_maillog l
where u.userid = b.userid
and u.userid = m.userid
and u.userid = l.userid
and l.senddate = m.senddate;
• The width of these rows is about 2k

77. Large SELECTs
• Application code does:
select u.*, b.browser, m.lastmess
from ods.ods_users u,
ods.ods_browsers b,
( select userid, min(senddate) as senddate
from ods.ods_maillog
group by userid ) m,
ods.ods_maillog l
where u.userid = b.userid
and u.userid = m.userid
and u.userid = l.userid
and l.senddate = m.senddate;
• The width of these rows is about 2k
• 50 million row return set

78. Large SELECTs
• Application code does:
select u.*, b.browser, m.lastmess
from ods.ods_users u,
ods.ods_browsers b,
( select userid, min(senddate) as senddate
from ods.ods_maillog
group by userid ) m,
ods.ods_maillog l
where u.userid = b.userid
and u.userid = m.userid
and u.userid = l.userid
and l.senddate = m.senddate;
• The width of these rows is about 2k
• 50 million row return set
• > 100 GB of data

79. The Large SELECT Problem
• libpq will buffer the entire result in memory.
• This affects language bindings (DBD::Pg).
• This is an utterly deficient default behavior.
• This can be avoided by using cursors
• Requires the app to be PostgreSQL specific.
• You open a cursor.
• Then FETCH the row count you desire.

80. Big SELECTs the Postgres way
The previous “big” query becomes:

81. Big SELECTs the Postgres way
The previous “big” query becomes:
DECLARE CURSOR bigdump FOR
select u.*, b.browser, m.lastmess
from ods.ods_users u,
ods.ods_browsers b,
( select userid, min(senddate) as senddate
from ods.ods_maillog
group by userid ) m,
ods.ods_maillog l
where u.userid = b.userid
and u.userid = m.userid
and u.userid = l.userid
and l.senddate = m.senddate;

82. Big SELECTs the Postgres way
The previous “big” query becomes:
DECLARE CURSOR bigdump FOR
select u.*, b.browser, m.lastmess
from ods.ods_users u,
ods.ods_browsers b,
( select userid, min(senddate) as senddate
from ods.ods_maillog
group by userid ) m,
ods.ods_maillog l
where u.userid = b.userid
and u.userid = m.userid
and u.userid = l.userid
and l.senddate = m.senddate;
Then, in a loop:
FETCH FORWARD 10000 FROM bigdump;

83. Autonomous Transactions

84. Autonomous Transactions
• In Oracle we have over 2000 custom stored procedures.

85. Autonomous Transactions
• In Oracle we have over 2000 custom stored procedures.
• During these procedures, we like to:

86. Autonomous Transactions
• In Oracle we have over 2000 custom stored procedures.
• During these procedures, we like to:
• COMMIT incrementally
Useful for long transactions (update/delete) that
need not be atomic -- incremental COMMITs.

87. Autonomous Transactions
• In Oracle we have over 2000 custom stored procedures.
• During these procedures, we like to:
• COMMIT incrementally
Useful for long transactions (update/delete) that
need not be atomic -- incremental COMMITs.
• start a new top-level txn that can COMMIT
Useful for logging progress in a stored procedure so
that you know how far you progessed and how long
each step took even if it rolls back.

88. PostgreSQL shortcoming

89. PostgreSQL shortcoming
• PostgreSQL simply does not support
Autonomous transactions and to quote core
developers “that would be hard.”

90. PostgreSQL shortcoming
• PostgreSQL simply does not support
Autonomous transactions and to quote core
developers “that would be hard.”
• When in doubt, use brute force.

91. PostgreSQL shortcoming
• PostgreSQL simply does not support
Autonomous transactions and to quote core
developers “that would be hard.”
• When in doubt, use brute force.
• Use pl/perl to use DBD::Pg to connect to
ourselves (a new backend) and execute a new
top-level transaction.

92. Replication

93. Replication
• Cross vendor database replication isn’t too difficult.

94. Replication
• Cross vendor database replication isn’t too difficult.
• Helps a lot when you can do it inside the database.

95. Replication
• Cross vendor database replication isn’t too difficult.
• Helps a lot when you can do it inside the database.
• Using dbi-link (based on pl/perl and DBI) we can.

96. Replication
• Cross vendor database replication isn’t too difficult.
• Helps a lot when you can do it inside the database.
• Using dbi-link (based on pl/perl and DBI) we can.
• We can connect to any remote database.

97. Replication
• Cross vendor database replication isn’t too difficult.
• Helps a lot when you can do it inside the database.
• Using dbi-link (based on pl/perl and DBI) we can.
• We can connect to any remote database.
• INSERT into local tables directly from remote
SELECT statements.
[snapshots]

98. Replication
• Cross vendor database replication isn’t too difficult.
• Helps a lot when you can do it inside the database.
• Using dbi-link (based on pl/perl and DBI) we can.
• We can connect to any remote database.
• INSERT into local tables directly from remote
SELECT statements.
[snapshots]
• LOOP over remote SELECT statements and
process them row-by-row.
[replaying remote DML logs]

99. Replication (really)

100. Replication (really)
• Through a combination of snapshotting and DML
replay we:
• replicate over into over 2000 tables in PostgreSQL
from Oracle
• snapshot replication of 200
• DML replay logs for 1800

101. Replication (really)
• Through a combination of snapshotting and DML
replay we:
• replicate over into over 2000 tables in PostgreSQL
from Oracle
• snapshot replication of 200
• DML replay logs for 1800
• PostgreSQL to Oracle is a bit harder
• out-of-band export and imports

102. New Architecture
• Master: Sun v890 and Hitachi AMS + warm standby
running Oracle
(1TB)
• Logs: several customs
running MySQL instances
(2TB each)
• ODS BI: 2x Sun v40
running PostgreSQL 8.3
(6TB on Sun JBODs on ZFS each)
• ODS archive: 2x custom
running PostgreSQL 8.3
(14TB internal storage on ZFS each)

103. PostgreSQL is Lacking
• No upgrades (AYFKM).
• pg_dump is too intrusive.
• Poor system-level instrumentation.
• Poor methods to determine specific contention.
• It relies on the operating system’s filesystem cache.
(which make PostgreSQL inconsistent across it’s
supported OS base)

104. Enter Solaris
• Solaris is a UNIX from Sun Microsystems.
• Is it different than other UNIX/UNIX-like systems?
• Mostly it isn’t different (hence the term UNIX)
• It does have extremely strong ABI backward
compatibility.
• It’s stable and works well on large machines.
• Solaris 10 shakes things up a bit:
• DTrace
• ZFS
• Zones

105. Solaris / ZFS
• ZFS: Zettaback Filesystem.
• 264 snapshots, 248 files/directory, 264 bytes/filesystem,
278 (256 ZiB) bytes in a pool, 264 devices/pool, 264 pools/system
• Extremely cheap differential backups.
• I have a 5 TB database, I need a backup!
• No rollback in your database? What is this? MySQL?
• No rollback in your filesystem?
• ZFS has snapshots, rollback, clone and promote.
• OMG! Life altering features.
• Caveat: ZFS is slower than alternatives, by about 10% with tuning.

106. Solaris / Zones
• Zones: Virtual Environments.
• Shared kernel.
• Can share filesystems.
• Segregated processes and privileges.
• No big deal for databases, right?
But Wait!

107. Solaris / ZFS + Zones = Magic Juju
https://labs.omniti.com/trac/pgsoltools/browser/trunk/pitr_clone/clonedb_startclone.sh
• ZFS snapshot, clone, delegate to zone, boot and run.
• When done, halt zone, destroy clone.
• We get a point-in-time copy of our PostgreSQL database:
• read-write,
• low disk-space requirements,
• NO LOCKS! Welcome back pg_dump,
you don’t suck (as much) anymore.
• Fast snapshot to usable copy time:
• On our 20 GB database: 1 minute.
• On our 1.2 TB database: 2 minutes.

108. ZFS: how I saved my soul.
• Database crash. Bad. 1.2 TB of data... busted.
The reason Robert Treat looks a bit older than he
should.
• xlogs corrupted. catalog indexes corrupted.
• Fault? PostgreSQL bug? Bad memory? Who knows?
• Trial & error on a 1.2 TB data set is a cruel experience.
• In real-life, most recovery actions are destructive
actions.
• PostgreSQL is no different.
• Rollback to last checkpoint (ZFS), hack postgres code,
try, fail, repeat.

109. Let DTrace open your eyes
• DTrace: Dynamic Tracing
• Dynamically instrument “stuff” in the system:
• system calls (like strace/truss/ktrace).
• process/scheduler activity (on/off cpu, semaphores, conditions).
• see signals sent and received.
• trace kernel functions, networking.
• watch I/O down to the disk.
• user-space processes, each function... each machine instruction!
• Add probes into apps where it makes sense to you.

110. Can you see what I see?
• There is EXPLAIN... when that isn’t enough...
• There is EXPLAIN ANALYZE... when that isn’t enough.
• There is DTrace.
; dtrace -q -n ‘
postgresql*:::statement-start
{
self->query = copyinstr(arg0);
self->ok=1;
}
io:::start
/self->ok/
{
@[self->query,
args[0]->b_flags & B_READ ? quot;readquot; : quot;writequot;,
args[1]->dev_statname] = sum(args[0]->b_bcount);
}’
dtrace: description 'postgres*:::statement-start' matched 14 probes
^C
select count(1) from c2w_ods.tblusers where zipcode between 10000 and 11000;
read sd1 16384
select division, sum(amount), avg(amount) from ods.billings where txn_timestamp
between ‘2006-01-01 00:00:00’ and ‘2006-04-01 00:00:00’ group by division;
read sd2 71647232

111. OmniTI Labs / pgsoltools
• https://labs.omniti.com/trac/pgsoltools
• Where we stick out PostgreSQL on Solaris goodies...
• like pg_file_stress
FILENAME/DBOBJECT READS WRITES
# min avg max # min avg max
alldata1__idx_remove_domain_external 1 12 12 12 398 0 0 0
slowdata1__pg_rewrite 1 12 12 12 0 0 0 0
slowdata1__pg_class_oid_index 1 0 0 0 0 0 0 0
slowdata1__pg_attribute 2 0 0 0 0 0 0 0
alldata1__mv_users 0 0 0 0 4 0 0 0
slowdata1__pg_statistic 1 0 0 0 0 0 0 0
slowdata1__pg_index 1 0 0 0 0 0 0 0
slowdata1__pg_index_indexrelid_index 1 0 0 0 0 0 0 0
alldata1__remove_domain_external 0 0 0 0 502 0 0 0
alldata1__promo_15_tb_full_2 19 0 0 0 11 0 0 0
slowdata1__pg_class_relname_nsp_index 2 0 0 0 0 0 0 0
alldata1__promo_177intaoltest_tb 0 0 0 0 1053 0 0 0
slowdata1__pg_attribute_relid_attnum_index 2 0 0 0 0 0 0 0
alldata1__promo_15_tb_full_2_pk 2 0 0 0 0 0 0 0
alldata1__all_mailable_2 1403 0 0 423 0 0 0 0
alldata1__mv_users_pkey 0 0 0 0 4 0 0 0

112. Results

113. Results
• Move ODS Oracle licenses to OLTP

114. Results
• Move ODS Oracle licenses to OLTP
• Run PostgreSQL on ODS

115. Results
• Move ODS Oracle licenses to OLTP
• Run PostgreSQL on ODS
• Save $800k in license costs.

116. Results
• Move ODS Oracle licenses to OLTP
• Run PostgreSQL on ODS
• Save $800k in license costs.
• Spend $100k in labor costs.

117. Results
• Move ODS Oracle licenses to OLTP
• Run PostgreSQL on ODS
• Save $800k in license costs.
• Spend $100k in labor costs.
• Learn a lot.

118. Thanks!
• Thank you.
• http://omniti.com/does/postgresql
• We’re hiring, but only if you love:
• lots of data on lots of disks on lots of big boxes
• smart people
• hard problems
• more than one database technology (including PostgreSQL)
• responsibility