This presentation covers a number of the way that you can tune PostgreSQL to better handle high write workloads. We will cover both application and database tuning methods as each type can have substantial benefits but can also interact in unexpected ways when you are operating at scale. On the application side we will look at write batching, use of GUID's, general index structure, the cost of additional indexes and impact of working set size. For the database we will see how wal compression, auto vacuum and checkpoint settings as well as a number of other configuration parameters can greatly affect the write performance of your database and application.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Grant McAlister – Senior Principal Engineer – Amazon RDS
Sept 2017
Tuning PostgreSQL for High
Write Workloads

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

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

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

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

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

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

19. 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

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

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

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

37. 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

46. 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

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

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

51. 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

73. 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

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

85. 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

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

103. Vacuum Freeze in Memory
Block in
Memory
WAL

104. Vacuum Freeze in Memory
Block in
Memory
WAL

105. Vacuum Freeze in Memory
Block in
Memory
WAL

106. Vacuum Freeze in Memory
Block in
Memory
WAL

107. 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

112. Vacuum Freeze in Memory
Block in
Memory
WAL

113. Vacuum Freeze in Memory
Block in
Memory
WAL

114. Vacuum Freeze in Memory
Block in
Memory
WAL

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

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

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

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

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

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

125. 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

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

129. 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

138. Concurrency
Queued Work
Log Buffer
PostgreSQL Aurora
Storage
A
Queued Work
Storage
B C D E
4 3 4 2 4
A B C D E
Durability
Tracking

139. Concurrency
Queued Work
Log Buffer
PostgreSQL Aurora
Storage
A
Queued Work
Storage
B C D E
4 3 4 2 4
A B C D E
Durability
Tracking

140. Concurrency
Queued Work
Log Buffer
PostgreSQL Aurora
Storage
A
Queued Work
Storage
B C D E
6 5 6 3 5
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

145. 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

146. 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

147. 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

149. 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

150. Thank you!
Questions?