Tuning PostgreSQL for High Write Throughput

2. High Write Workloads? CLIENT Database Storage Single Insert/Update

3. High Write Workloads? CLIENT Database Storage Commit Latency Client Latency Single Insert/Update

4. High Write Workloads? CLIENT Database Storage Commit Latency Client Latency CLIENT Database Storage Single Insert/Update

5. High Write Workloads? CLIENT Database Storage Commit Latency Client Latency CLIENT Database Storage Commit Latency Client Latency Single Insert/Update Copy or Multi-Insert

6. High Write Workloads? CLIENT Database Storage Commit Latency Client Latency CLIENT Database Storage Commit Latency Client Latency Single Insert/Update Copy or Multi-Insert CLIENT Database Storage

7. High Write Workloads? CLIENT Database Storage Commit Latency Client Latency CLIENT Database Storage Commit Latency Client Latency Single Insert/Update Copy or Multi-Insert CLIENT Database Storage Commit Latency Many Clients T H R O U G H P U T

8. Insert Test Test Table • UUID PK – Random • ID int – Right Lean Sequence • VARCHAR(100) – Random • VARCHAR(50) – Small Set of Words • INT – Random • INT – Random (smaller set) • BOOLEAN – Random (50/50) • BOOLEAN – Somewhat Random (75/25) • Timestamp – Right Lean

9. 0 5,000 10,000 15,000 20,000 25,000 30,000 1 31 61 91 121 151 181 211 241 271 InsertsPerSecond Minutes Insert Workload- PostgreSQL 9.6 BASE Non Random GUID WHICH ONE WOULD YOU LIKE

10. Update Test Test Table • UUID PK – Random • ID int – Right Lean Sequence • VARCHAR(100) – Random • VARCHAR(50) – Small Set of Words • INT – Random • INT – Random (smaller set) • BOOLEAN – Random (50/50) • BOOLEAN – Somewhat Random (75/25) • Timestamp – Right Lean UPDATE #1 UPDATE #2

11. 3,729 3,949 4,871 4,302 9,177 10,290 17,158 - 5,000 10,000 15,000 20,000 TPS(2UpdatesperTransaction) Update Workload - PostgreSQL 9.6 BASE WAL Compression 16GB Max WAL Async Reduced Indexes Non Random GUID Aurora PostgreSQL WHICH ONE WOULD YOU LIKE

12. Parameter Tuning

13. Concurrency and Throughput – Log Buffer Queued Work Log Buffer Most Databases Storage

20. Full Page Writes Block in Memory PostgreSQL WAL

21. Full Page Writes Block in Memory PostgreSQL update t set y = 6; WAL

22. Full Page Writes Block in Memory PostgreSQL update t set y = 6; Full Block WAL

23. Full Page Writes Block in Memory PostgreSQL update t set y = 6; Full Block WAL

24. Full Page Writes Block in Memory PostgreSQL update t set y = 6; Checkpoint Datafile Full Block WAL

25. Full Page Writes Block in Memory PostgreSQL update t set y = 6; Checkpoint Datafile Full Block WAL Archive

26. Full Page Writes Block in Memory PostgreSQL update t set y = 6; Checkpoint Datafile Full Block WAL Archive 4K 4K 8K

27. Full Page Writes Block in Memory PostgreSQL update t set y = 6; Checkpoint Datafile Full Block WAL Archive 4K 4K 8K During crash recovery PostgreSQL uses the FPW block in the WAL to replace the bad checkpointed block

28. Full Page Writes Block in Memory PostgreSQL update t set y = 6; Checkpoint Datafile Full Block WAL Archive 4K 4K 8K During crash recovery PostgreSQL uses the FPW block in the WAL to replace the bad checkpointed block

29. WAL throughput – Dump of wal Btree d: INSERT_LEAF off 184, blkref #0: rel 1663/32772/32779 blk 300 Btree d: INSERT_LEAF off 110, blkref #0: rel 1663/32772/32784 blk 1092 Btree d: INSERT_LEAF off 41, blkref #0: rel 1663/32772/32782 blk 5752 Btree d: INSERT_LEAF off 40, blkref #0: rel 1663/32772/32782 blk 8000 Btree d: INSERT_LEAF off 89, blkref #0: rel 1663/32772/32779 blk 1757 Btree d: INSERT_LEAF off 363, blkref #0: rel 1663/32772/32781 blk 1355 Btree d: INSERT_LEAF off 77, blkref #0: rel 1663/32772/32783 blk 4 Btree d: INSERT_LEAF off 94, blkref #0: rel 1663/32772/32779 blk 2083 Btree d: INSERT_LEAF off 362, blkref #0: rel 1663/32772/32781 blk 1355 Btree d: INSERT_LEAF off 10, blkref #0: rel 1663/32772/32782 blk 7687 Btree d: INSERT_LEAF off 365, blkref #0: rel 1663/32772/32781 blk 1355 Btree d: INSERT_LEAF off 114, blkref #0: rel 1663/32772/32784 blk 791 Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/32772/32783 blk 2213 Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/32772/32785 blk 1639 Btree d: INSERT_LEAF off 209, blkref #0: rel 1663/32772/32784 blk 1433 Transaction d: COMMIT 2017-09-07 01:08:55.354810 UTC At the beginning of the run

30. WAL throughput – Dump of wal Btree d: INSERT_LEAF off 184, blkref #0: rel 1663/32772/32779 blk 300 Btree d: INSERT_LEAF off 110, blkref #0: rel 1663/32772/32784 blk 1092 Btree d: INSERT_LEAF off 41, blkref #0: rel 1663/32772/32782 blk 5752 Btree d: INSERT_LEAF off 40, blkref #0: rel 1663/32772/32782 blk 8000 Btree d: INSERT_LEAF off 89, blkref #0: rel 1663/32772/32779 blk 1757 Btree d: INSERT_LEAF off 363, blkref #0: rel 1663/32772/32781 blk 1355 Btree d: INSERT_LEAF off 77, blkref #0: rel 1663/32772/32783 blk 4 Btree d: INSERT_LEAF off 94, blkref #0: rel 1663/32772/32779 blk 2083 Btree d: INSERT_LEAF off 362, blkref #0: rel 1663/32772/32781 blk 1355 Btree d: INSERT_LEAF off 10, blkref #0: rel 1663/32772/32782 blk 7687 Btree d: INSERT_LEAF off 365, blkref #0: rel 1663/32772/32781 blk 1355 Btree d: INSERT_LEAF off 114, blkref #0: rel 1663/32772/32784 blk 791 Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/32772/32783 blk 2213 Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/32772/32785 blk 1639 Btree d: INSERT_LEAF off 209, blkref #0: rel 1663/32772/32784 blk 1433 Transaction d: COMMIT 2017-09-07 01:08:55.354810 UTC Btree d: INSERT_LEAF off 216, blkref #0: rel 1663/16395/16407 blk 14331 FPW Btree d: INSERT_LEAF off 123, blkref #0: rel 1663/16395/16406 blk 5 Btree d: INSERT_LEAF off 139, blkref #0: rel 1663/16395/16404 blk 25954 Btree d: INSERT_LEAF off 59, blkref #0: rel 1663/16395/16407 blk 17944 FPW Btree d: INSERT_LEAF off 45, blkref #0: rel 1663/16395/16408 blk 17 Btree d: INSERT_LEAF off 252, blkref #0: rel 1663/16395/16404 blk 25954 Btree d: INSERT_LEAF off 135, blkref #0: rel 1663/16395/16408 blk 7 Btree d: INSERT_LEAF off 5, blkref #0: rel 1663/16395/16405 blk 131373 FPW Btree d: INSERT_LEAF off 175, blkref #0: rel 1663/16395/16404 blk 25954 Btree d: INSERT_LEAF off 19, blkref #0: rel 1663/16395/16405 blk 40974 FPW Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/16395/16409 blk 1 Btree d: INSERT_LEAF off 19, blkref #0: rel 1663/16395/16405 blk 143873 FPW Btree d: INSERT_LEAF off 123, blkref #0: rel 1663/16395/16406 blk 5 Btree d: INSERT_LEAF off 14, blkref #0: rel 1663/16395/16405 blk 37468 FPW Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/16395/16409 blk 1 Btree d: INSERT_LEAF off 84, blkref #0: rel 1663/16395/16407 blk 2696 Btree d: INSERT_LEAF off 149, blkref #0: rel 1663/16395/16407 blk 1401 FPW Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/16395/16406 blk 39718 Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/16395/16410 blk 29411 Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/16395/16408 blk 29370 Btree d: INSERT_LEAF off 123, blkref #0: rel 1663/16395/16406 blk 5 Btree d: INSERT_LEAF off 2, blkref #0: rel 1663/16395/16409 blk 1 Btree d: INSERT_LEAF off 24, blkref #0: rel 1663/16395/16405 blk 69991 FPW Transaction d: COMMIT 2017-09-07 01:04:32.650362 UTC At the beginning of the run Later in the run 1K vs 48K of data

31. WAL Compression Block in Memory update t set y = 6; Full Block WAL

32. WAL Compression Block in Memory update t set y = 6; WAL Compressed Block

33. WAL Compression Block in Memory update t set y = 6; WAL Compressed BlockCompressed Block

38. WAL Compression Block in Memory update t set y = 6; Block on Disk Checkpoint Datafile WAL Archive Compressed BlockCompressed Block Lot of Random Values = Poor Compression

39. 0 5,000 10,000 15,000 20,000 25,000 30,000 1 31 61 91 121 151 181 211 241 271 InsertsPerSecond Minutes Insert Workload- PostgreSQL 9.6 BASE WAL Compression

40. 3,729 3,949 4,871 4,302 9,177 10,290 17,158 - 5,000 10,000 15,000 20,000 TPS(2UpdatesperTransaction) Update Workload - PostgreSQL 9.6 BASE WAL Compression 16GB Max WAL Async Reduced Indexes Non Random GUID Aurora PostgreSQL

41. Why didn’t WAL compression help Regular – Average 5.7KB per FPW

42. Why didn’t WAL compression help Regular – Average 5.7KB per FPW Compressed – Average 5.2KB per FPW

43. Why didn’t WAL compression help Regular – Average 5.7KB per FPW Compressed – Average 5.2KB per FPW Random Data does not compress well

44. 0 5,000 10,000 15,000 20,000 25,000 30,000 1 31 61 91 121 151 181 211 241 271 InsertsPerSecond Minutes Insert Workload- PostgreSQL 9.6 BASE WAL Compression 16GB Max WAL max_wal_size=16GB More Blocks + Random Inserts = More FPW

45. Randomness and Size of the Data Assume 10K Random Updates per Second Assume checkpoint every 60 second Touch 10K x 60 = 600K blocks between checkpoints 1GB table is ~130K blocks - Touch every block 4 times 100GB table is 13M blocks - Low chance of same block touch

47. 3,729 3,949 4,871 4,302 9,177 10,290 17,158 - 5,000 10,000 15,000 20,000 TPS(2UpdatesperTransaction) Update Workload - PostgreSQL 9.6 BASE WAL Compression 16GB Max WAL Async Reduced Indexes Non Random GUID Aurora PostgreSQL What about ASYNC

48. Async Commit – Log Buffer Queued Work Log Buffer Most Databases Storage

49. Async Commit – Log Buffer Queued Work Log Buffer Most Databases StorageThese sessions no longer have to wait

52. Longer Checkpoints – Reduce FPW - BUT Default min_wal_size=256MB, max_wal_size=2GB recovery time = 3 seconds New min_wal_size=2GB, max_wal_size=16GB recovery time = 91 seconds

53. Non Parameter Tuning

54. 1- 200 1- 100 101- 200 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert a Sequence Number into a B-tree

55. 1- 200 1- 100 101- 200 Insert 201 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 1- 201 101- 201 151- 201 176- 201 4 blocks loaded Insert a Sequence Number into a B-tree

56. 1- 200 1- 100 101- 200 Insert 201 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert 202 1- 201 101- 201 151- 201 176- 201 1- 202 101- 202 151- 202 176- 202 4 blocks loaded Insert a Sequence Number into a B-tree 0 blocks loaded

57. 1- 200 1- 100 101- 200 Insert 201 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert 202 1- 201 101- 201 151- 201 176- 201 1- 202 101- 202 151- 202 176- 202 4 blocks loaded Insert a Sequence Number into a B-tree 0 blocks loaded At least 1 FPW

58. 1- 200 1- 100 101- 200 Insert 124 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 1- 200 101- 200 101- 150 101- 125 4 blocks loaded Insert a Random value into a B-tree

59. 1- 200 1- 100 101- 200 Insert 124 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert 99 1- 200 101- 200 101- 150 101- 125 4 blocks loaded 1- 100 51- 100 76- 100 3 blocks loaded Insert a Random value into a B-tree

60. 1- 200 1- 100 101- 200 Insert 124 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert 99 1- 200 101- 200 101- 150 101- 125 4 blocks loaded 1- 100 51- 100 76- 100 3 blocks loaded Insert a Random value into a B-tree 151- 200 151- 200 2 blocks loaded Insert 161 At least 3 FPW

61. Remember those 9 Indexes – Cut it to 6 Test Table • UUID PK – Random • ID int – Right Lean Sequence • VARCHAR(100) – Random • VARCHAR(50) – Small Set of Words • INT – Random • INT – Random (smaller set) • BOOLEAN – Random (50/50) • BOOLEAN – Somewhat Random (75/25) • Timestamp – Right Lean Remove Index Remove Index Remove Index – Allows HOT Update

62. HOT (Heap-Only-Tuple) Update Not HOT a b f Indexes Heap a, b, c, d, e, f

63. HOT (Heap-Only-Tuple) Update Not HOT a b f Indexes Heap a, b, c, d, e, f a, b, c, d, e, f1

64. HOT (Heap-Only-Tuple) Update Not HOT a b f Indexes Heap a, b, c, d, e, f a, b, c, d, e, f1 a’ b’

65. HOT (Heap-Only-Tuple) Update Not HOT a b f Indexes Heap a, b, c, d, e, f a, b, c, d, e, f1 a’ b’ f1

66. HOT (Heap-Only-Tuple) Update Not HOT a b f Indexes Heap a, b, c, d, e, f a, b, c, d, e, f1 a’ b’ f1 a, b, c1, d, e, f

67. HOT (Heap-Only-Tuple) Update Not HOT a b f Indexes Heap a, b, c, d, e, f a, b, c, d, e, f1 a’ b’ f1 a, b, c1, d, e, f HOT a b f Indexes Heap a, b, c, d, e, f

68. HOT (Heap-Only-Tuple) Update Not HOT a b f Indexes Heap a, b, c, d, e, f a, b, c, d, e, f1 a’ b’ f1 a, b, c1, d, e, f HOT a b f Indexes Heap a, b, c, d, e, f a, b, c1, d, e, f

69. HOT (Heap-Only-Tuple) Update Not HOT a b f Indexes Heap a, b, c, d, e, f a, b, c, d, e, f1 a’ b’ f1 a, b, c1, d, e, f HOT a b f Indexes Heap a, b, c, d, e, f a, b, c1, d, e, f

70. HOT Updates – Looking at FPW in the logs HOT Updated Heap 14/ 68, , d: HOT_UPDATE off 19 xmax 2327993188 ; new off 3 xmax 0, blkref #0: rel 1663/41083/41086 blk 28 XLOG 0/ 3368, , d: FPI_FOR_HINT , blkref #0: rel 1663/41083/41092 blk 1492899 FPW Transaction 8/ 34, , d: COMMIT 2017-09-07 00:07:17.532647 UTC Non HOT Update Heap 14/ 75, , d: UPDATE off 67 xmax 2327993195 ; new off 7 xmax 0, blkref #0: rel 1663/41083/41086 blk 285 XLOG 0/ 2774, , d: FPI_FOR_HINT , blkref #0: rel 1663/41083/41090 blk 7039952 FPW Btree 2/ 120, , d: INSERT_LEAF off 17, blkref #0: rel 1663/41083/41090 blk 7039952 XLOG 0/ 3150, , d: FPI_FOR_HINT , blkref #0: rel 1663/41083/41092 blk 29 FPW Btree 2/ 64, , d: INSERT_LEAF off 205, blkref #0: rel 1663/41083/41092 blk 29 Btree 2/ 2639, , d: INSERT_LEAF off 73, blkref #0: rel 1663/41083/41093 blk 4 FPW Btree 2/ 3148, , d: INSERT_LEAF off 2, blkref #0: rel 1663/41083/41094 blk 1 FPW Btree 2/ 5099, , d: INSERT_LEAF off 364, blkref #0: rel 1663/41083/41095 blk 4237904 FPW Transaction 8/ 34, , d: COMMIT 2017-09-07 00:24:29.427017 UTC 3.4K VS 16.7K

71. HOT Updates – How to Track sfo=> select n_tup_upd, n_tup_hot_upd from pg_stat_all_tables where relname = 'benchmark_uuid'; n_tup_upd | n_tup_hot_upd -----------+--------------- 0 | 0 sfo=> update benchmark_uuid set e=cast(0 as boolean) where id = 1000; UPDATE 1 sfo=> select n_tup_upd, n_tup_hot_upd from pg_stat_all_tables where relname = 'benchmark_uuid'; n_tup_upd | n_tup_hot_upd -----------+--------------- 1 | 1 sfo=> update benchmark_uuid set last_updated=CURRENT_TIMESTAMP where id=1001; UPDATE 1 sfo=> select n_tup_upd, n_tup_hot_upd from pg_stat_all_tables where relname = 'benchmark_uuid'; n_tup_upd | n_tup_hot_upd -----------+--------------- 2 | 1 HOT UPDATE = !=

72. 0 5,000 10,000 15,000 20,000 25,000 30,000 1 31 61 91 121 151 181 211 241 271 InsertsPerSecond Minutes Insert Workload- PostgreSQL 9.6 BASE WAL Compression 16GB Max WAL Reduced Indexes

74. Remember those 9 Indexes – Cut it to 6 Test Table • UUID PK – Random • ID int – Right Lean Sequence • VARCHAR(100) – Random • VARCHAR(50) – Small Set of Words • INT – Random • INT – Random (smaller set) • BOOLEAN – Random (50/50) • BOOLEAN – Somewhat Random (75/25) • Timestamp – Right Lean

75. 1- 200 1- 100 101- 200 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert a Constrained Random Value into a B-tree

76. 1- 200 1- 100 101- 200 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert a Constrained Random Value into a B-tree

77. 1- 200 1- 100 101- 200 Insert 172 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 1- 200 101- 200 151- 200 151- 175 4 blocks loaded Insert a Constrained Random Value into a B-tree

78. 1- 200 1- 100 101- 200 Insert 172 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert 199 1- 200 101- 200 151- 200 151- 175 4 blocks loaded 176- 200 1 blocks loaded Insert a Constrained Random Value into a B-tree Insert 161

79. 1- 200 1- 100 101- 200 Insert 172 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert 199 1- 200 101- 200 151- 200 151- 175 4 blocks loaded 176- 200 1 blocks loaded Insert a Constrained Random Value into a B-tree Insert 161 0 blocks loaded

80. 1- 200 1- 100 101- 200 Insert 172 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert 199 1- 200 101- 200 151- 200 151- 175 4 blocks loaded 176- 200 1 blocks loaded Insert a Constrained Random Value into a B-tree Insert 161 0 blocks loaded Insert 168 0 blocks loaded

81. 1- 200 1- 100 101- 200 Insert 172 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 Insert 199 1- 200 101- 200 151- 200 151- 175 4 blocks loaded 176- 200 1 blocks loaded Insert a Constrained Random Value into a B-tree Insert 161 0 blocks loaded Insert 168 0 blocks loaded At least 2 FPW

82. Constraining Random Values Prefix UUID with a date • 550e8400-e29b-41d4-a716-446655440000 • YYYYMMDDHH24-UUID • Example • 2010022712-550e8400-e29b-41d4-a716-446655440000 • 2010022713-550e8400-e29b-41d4-a716-446655440000 • Balance the number of hot blocks vs contention • More date precision • less random to b-tree = less blocks touched • Possibly more contention on the leaf blocks

83. Change PK to UUID like and remove 3 indexes Test Table • UUID PK – Random • ID int – Right Lean Sequence • VARCHAR(100) – Random • VARCHAR(50) – Small Set of Words • INT – Random • INT – Random (smaller set) • BOOLEAN – Random (50/50) • BOOLEAN – Somewhat Random (75/25) • Timestamp – Right Lean Remove Index Remove Index Remove Index – Allows HOT Update Remove Index Remove Index Make non random – right lean

84. 0 5,000 10,000 15,000 20,000 25,000 30,000 1 31 61 91 121 151 181 211 241 271 InsertsPerSecond Minutes Insert Workload- PostgreSQL 9.6 BASE WAL Compression 16GB Max WAL Reduced Indexes Non Random GUID

86. Why Vacuuming Matter tuple1 tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1

87. Why Vacuuming Matter tuple1 tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1 tuple2 tuple2

88. Why Vacuuming Matter tuple1 tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1 tuple2 tuple2 tuple7

89. Why Vacuuming Matter tuple1 tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1 tuple2 tuple2 tuple7 tuple8 tuple9 tuple10

90. Why Vacuuming Matter tuple1 tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1 tuple2 tuple2 tuple7 tuple8 tuple9 tuple10 Run Vacuum

91. Why Vacuuming Matter tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1 tuple2 tuple2 tuple7 tuple8 tuple9 tuple10 Run Vacuum

92. Why Vacuuming Matter tuple3 tuple4 tuple5 tuple6 tuple1 tuple2 tuple7 tuple8 tuple9 tuple10 Run Vacuum

93. Why Vacuuming Matter tuple3 tuple4 tuple5 tuple6 tuple1 tuple2 tuple7 tuple8 tuple9 tuple10 Run Vacuumtuple3 tuple3

94. Why Vacuuming Matter tuple3 tuple4 tuple5 tuple6 tuple1 tuple2 tuple7 tuple8 tuple9 tuple10 Run Vacuumtuple3 tuple4 tuple3 tuple4

95. Why Vacuuming Matter tuple3 tuple4 tuple5 tuple6 tuple1 tuple2 tuple7 tuple8 tuple9 tuple10 Run Vacuumtuple3 tuple4 tuple3 tuple4 tuple1 tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1 tuple2 tuple2 tuple7 tuple8 tuple9 tuple10 No Vacuum

96. Why Vacuuming Matter tuple3 tuple4 tuple5 tuple6 tuple1 tuple2 tuple7 tuple8 tuple9 tuple10 Run Vacuumtuple3 tuple4 tuple3 tuple4 tuple1 tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1 tuple2 tuple2 tuple7 tuple8 tuple9 tuple10 No Vacuumtuple3 tuple3

97. Why Vacuuming Matter tuple3 tuple4 tuple5 tuple6 tuple1 tuple2 tuple7 tuple8 tuple9 tuple10 Run Vacuumtuple3 tuple4 tuple3 tuple4 tuple1 tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1 tuple2 tuple2 tuple7 tuple8 tuple9 tuple10 No Vacuumtuple3 tuple4 tuple3 tuple4

98. Why Vacuuming Matter tuple3 tuple4 tuple5 tuple6 tuple1 tuple2 tuple7 tuple8 tuple9 tuple10 Run Vacuumtuple3 tuple4 tuple3 tuple4 tuple1 tuple2 tuple3 tuple4 tuple5 tuple6 tuple1tuple1 tuple2 tuple2 tuple7 tuple8 tuple9 tuple10 No Vacuumtuple3 tuple4 tuple3 tuple4 More blocks = More Cache Misses,non HOT updates & more FPW

99. 4,000 5,000 6,000 7,000 8,000 9,000 10,000 1 61 121 181 241 301 361 421 481 541 601 661 721 781 841 901 961 1021 1081 1141 1201 1261 TPS Minutes Updates - No Vacuum Running

100. Vacuuming in Memory – Insert Only Workload HEAP PAGES

101. Vacuuming in Memory – Insert Only Workload HEAP PAGES 1- 200 1- 100 101- 200 1-50 51- 100 1-25 26- 50 51- 75 76- 100 101- 150 151- 200 101- 125 126- 150 151- 175 176- 200 1- 201 101- 201 151- 201 176- 201 1- 202 101- 202 151- 202 176- 202 NO LONG RUNNING TRANSACTIONS

102. Vacuum Freeze in Memory Block in Memory WAL

108. Vacuum Freeze in Memory Block in Memory Checkpoint Datafile WAL Archive Not Frozen

109. Vacuum Freeze in Memory Block in Memory Checkpoint Datafile WAL Archive Not Frozen

110. Vacuum Freeze in Memory Block in Memory Checkpoint Datafile WAL Archive Not Frozen VACUUM

111. Vacuum Freeze in Memory Block in Memory Checkpoint Datafile Full Block WAL Archive Not Frozen VACUUM

115. Vacuum Freeze in Memory Block in Memory Checkpoint Datafile WAL Archive Frozen

116. Vacuum in Memory Continued • Increase checkpoint_timeout • alter table X set (vacuum settings); • Manual Test • Vacuum in Memory before checkpoint – 3.5 seconds • Vacuum in Memory after checkpoint – 84.5 seconds • Vacuum not in Memory – 165.8 seconds

117. Aurora PostgreSQL

118. Aurora PostgreSQL Differences • No Checkpoints • No Full Page Writes (FPW) • No Log Buffer

119. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage Queued Work Storage

126. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage

127. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage B

130. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage B C

131. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage B C

132. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage B C D

133. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage B C D

134. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage B C D E

135. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage B C D E

136. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage B C D E 0 0 0 0 0 A B C D E Durability Tracking

137. Concurrency Queued Work Log Buffer PostgreSQL Aurora Storage A Queued Work Storage B C D E 0 0 0 0 0 A B C D E 2 2 1 0 1 A B C D E Durability Tracking

141. Aurora – Writing Less Block in Memory PostgreSQL Aurora WAL Block in Memory Aurora Storage

142. Aurora – Writing Less Block in Memory PostgreSQL Aurora update t set y = 6; Full Block WAL Block in Memory Aurora Storage

143. Aurora – Writing Less Block in Memory PostgreSQL Aurora update t set y = 6; Full Block WAL Block in Memory Aurora Storage

144. Aurora – Writing Less Block in Memory PostgreSQL Aurora update t set y = 6; update t set y = 6; Checkpoint Datafile Full Block WAL Archive Block in Memory Aurora Storage

148. 0 5,000 10,000 15,000 20,000 25,000 30,000 1 31 61 91 121 151 181 211 241 271 InsertsPerSecond Minutes Insert Workload- PostgreSQL 9.6 BASE WAL Compression 16GB Max WAL Reduced Indexes Non Random GUID Aurora PostgreSQL

150. Thank you! Questions?

Tuning PostgreSQL for High Write Throughput

Grant McAlister

Tuning PostgreSQL for High Write Throughput