UploadLanguage (EN)Support
BusinessMobileSocial MediaMarketingTechnologyArt & PhotosCareerDesignEducationPresentations & Public SpeakingGovernment & NonprofitHealthcareInternetLawLeadership & ManagementAutomotiveEngineeringSoftwareRecruiting & HRRetailSalesServicesScienceSmall Business & EntrepreneurshipFoodEnvironmentEconomy & FinanceData & AnalyticsInvestor RelationsSportsSpiritualNews & PoliticsTravelSelf ImprovementReal EstateEntertainment & HumorHealth & MedicineDevices & HardwareLifestyle
Upload
Sign in
Alexander Korotkov, profile picture
Uploaded byAlexander Korotkov
PDF, PPTX5,347 views

Solving PostgreSQL wicked problems

The document discusses the persistent challenges ('wicked problems') faced by PostgreSQL, including issues with MVCC, failover data loss, and inefficient replication. It highlights criticisms of PostgreSQL while outlining potential solutions, particularly through the development of the OrioleDB extension, designed to address scalability and performance issues. Benchmarks indicate that OrioleDB significantly enhances transaction throughput compared to standard PostgreSQL under various conditions.

Software◦
Download as PDF, PPTX
1 / 61
2 / 61
3 / 61
4 / 61
5 / 61
6 / 61
7 / 61
8 / 61
9 / 61
10 / 61
Most read
11 / 61
12 / 61
13 / 61
14 / 61
15 / 61
16 / 61
Most read
17 / 61
18 / 61
19 / 61
20 / 61
Solving PostgreSQL wicked problems Alexander Korotkov Oriole DB Inc. 2021 Alexander Korotkov Solving PostgreSQL wicked problems 1 / 40
PostgreSQL has two sides Alexander Korotkov Solving PostgreSQL wicked problems 2 / 40
The bright side of PostgreSQL Alexander Korotkov Solving PostgreSQL wicked problems 3 / 40
PostgreSQL – one of the most popular DBMS’es1 1 According to db-engines.com Alexander Korotkov Solving PostgreSQL wicked problems 4 / 40
PostgreSQL – strong trend2 2 https://db-engines.com/en/ranking_trend/system/PostgreSQL Alexander Korotkov Solving PostgreSQL wicked problems 5 / 40
PostgreSQL – most loved RDBMS3 3 According to Stackoverflow 2020 survey Alexander Korotkov Solving PostgreSQL wicked problems 6 / 40
The dark side of PostgreSQL Alexander Korotkov Solving PostgreSQL wicked problems 7 / 40
Cri cism of PostgreSQL (1/2) https://eng.uber.com/postgres-to-mysql-migration/ Alexander Korotkov Solving PostgreSQL wicked problems 8 / 40
Cri cism of PostgreSQL (2/2) https://medium.com/@rbranson/10-things-i-hate-about-postgresql-20dbab8c2791 Alexander Korotkov Solving PostgreSQL wicked problems 9 / 40
10 wicked problems of PostgreSQL Problem name Known for Work started Resolu on 1. Wraparound 20 years 15 years ago S ll WIP 2. Failover Will Probably Lose Data 20 years 16 years ago S ll WIP 3. Inefficient Replica on That Spreads Corrup on 10 years 8 years ago S ll WIP 4. MVCC Garbage Frequently Painful 20 years 19 years ago Abandoned 5. Process-Per-Connec on = Pain at Scale 20 years 3 years ago Abandoned 6. Primary Key Index is a Space Hog 13 years — Not started 7. Major Version Upgrades Can Require Down me 21 years 16 years ago S ll WIP 8. Somewhat Cumbersome Replica on Setup 10 years 9 years ago S ll WIP 9. Ridiculous No-Planner-Hints Dogma 20 years 11 years ago Extension 10. No Block Compression 12 years 11 years ago S ll WIP * Scalability on modern hardware Alexander Korotkov Solving PostgreSQL wicked problems 10 / 40
The exci ng moment ▶ PostgreSQL community have proven to be brilliant on solving non-design issues, providing fantas c product to the market. Alexander Korotkov Solving PostgreSQL wicked problems 11 / 40
The exci ng moment ▶ PostgreSQL community have proven to be brilliant on solving non-design issues, providing fantas c product to the market. ▶ As a result, PostgreSQL has had a strong upwards trend for many years. Alexander Korotkov Solving PostgreSQL wicked problems 11 / 40
The exci ng moment ▶ PostgreSQL community have proven to be brilliant on solving non-design issues, providing fantas c product to the market. ▶ As a result, PostgreSQL has had a strong upwards trend for many years. ▶ At the same me, the PostgreSQL community appears to be dysfunc onal in solving design issues, a rac ng severe cri cism. Nevertheless, cri cs not yet break the upwards trend. Alexander Korotkov Solving PostgreSQL wicked problems 11 / 40
The exci ng moment ▶ PostgreSQL community have proven to be brilliant on solving non-design issues, providing fantas c product to the market. ▶ As a result, PostgreSQL has had a strong upwards trend for many years. ▶ At the same me, the PostgreSQL community appears to be dysfunc onal in solving design issues, a rac ng severe cri cism. Nevertheless, cri cs not yet break the upwards trend. ▶ It appears to be a unique moment for PostgreSQL redesign! Alexander Korotkov Solving PostgreSQL wicked problems 11 / 40
How could we solve the PostgreSQL wicked problems? Alexander Korotkov Solving PostgreSQL wicked problems 12 / 40
Tradi onal buffer management 1 2 3 4 5 6 7 Disk 1' 2' 3' 5' 6' Memory Buffer mapping 1 2 3 5 6 4 7 ▶ Each page access requires lookup into buffer mapping data structure. Alexander Korotkov Solving PostgreSQL wicked problems 13 / 40
Tradi onal buffer management 1 2 3 4 5 6 7 Disk 1' 2' 3' 5' 6' Memory Buffer mapping 1 2 3 5 6 4 7 ▶ Each page access requires lookup into buffer mapping data structure. ▶ Each B-tree key lookup takes mul ple buffer mapping lookups. Alexander Korotkov Solving PostgreSQL wicked problems 13 / 40
Tradi onal buffer management 1 2 3 4 5 6 7 Disk 1' 2' 3' 5' 6' Memory Buffer mapping 1 2 3 5 6 4 7 ▶ Each page access requires lookup into buffer mapping data structure. ▶ Each B-tree key lookup takes mul ple buffer mapping lookups. ▶ Accessing cached data doesn’t scale on modern hardware. Alexander Korotkov Solving PostgreSQL wicked problems 13 / 40
Solu on: Dual pointers 1 2 3 5 7 1 2 3 4 5 6 7 Disk ▶ In-memory page refers either in-memory or on-disk page. Alexander Korotkov Solving PostgreSQL wicked problems 14 / 40
Solu on: Dual pointers 1 2 3 5 7 1 2 3 4 5 6 7 Disk ▶ In-memory page refers either in-memory or on-disk page. ▶ Accessing cached data without buffer mapping lookups. Alexander Korotkov Solving PostgreSQL wicked problems 14 / 40
Solu on: Dual pointers 1 2 3 5 7 1 2 3 4 5 6 7 Disk ▶ In-memory page refers either in-memory or on-disk page. ▶ Accessing cached data without buffer mapping lookups. ▶ Good scalability! Alexander Korotkov Solving PostgreSQL wicked problems 14 / 40
PostgreSQL MVCC = bloat + write-amplifica on ▶ New and old row versions shares the same heap. Alexander Korotkov Solving PostgreSQL wicked problems 15 / 40
PostgreSQL MVCC = bloat + write-amplifica on ▶ New and old row versions shares the same heap. ▶ Non-HOT updates cause index bloat. Alexander Korotkov Solving PostgreSQL wicked problems 15 / 40
Solu on: undo log for both pages and rows Undo row1 row4 row1 row2 row3 row4 page row2v1 row1v1 row1v2 ▶ Old row versions form chains in undo log. Alexander Korotkov Solving PostgreSQL wicked problems 16 / 40
Solu on: undo log for both pages and rows Undo row1 row4 row1 row2 row3 row4 page row2v1 row1v1 row1v2 ▶ Old row versions form chains in undo log. ▶ Page-level chains evict deleted rows from primary storage. Alexander Korotkov Solving PostgreSQL wicked problems 16 / 40
Solu on: undo log for both pages and rows Undo row1 row4 row1 row2 row3 row4 page row2v1 row1v1 row1v2 ▶ Old row versions form chains in undo log. ▶ Page-level chains evict deleted rows from primary storage. ▶ Update only indexes with changed values. Alexander Korotkov Solving PostgreSQL wicked problems 16 / 40
Block-level WAL Heap Index #1 Index #2 WAL ▶ Huge WAL traffic. Alexander Korotkov Solving PostgreSQL wicked problems 17 / 40
Block-level WAL Heap Index #1 Index #2 WAL ▶ Huge WAL traffic. ▶ Problems with parallel apply. Alexander Korotkov Solving PostgreSQL wicked problems 17 / 40
Block-level WAL Heap Index #1 Index #2 WAL ▶ Huge WAL traffic. ▶ Problems with parallel apply. ▶ Not suitable for mul -master replica on. Alexander Korotkov Solving PostgreSQL wicked problems 17 / 40
Solu on: row-level WAL Heap Index #1 Index #2 WAL ▶ Very compact. Alexander Korotkov Solving PostgreSQL wicked problems 18 / 40
Solu on: row-level WAL Heap Index #1 Index #2 WAL ▶ Very compact. ▶ Apply can be parallelized. Alexander Korotkov Solving PostgreSQL wicked problems 18 / 40
Solu on: row-level WAL Heap Index #1 Index #2 WAL ▶ Very compact. ▶ Apply can be parallelized. ▶ Suitable for mul master (row-level conflicts, not block-level). Alexander Korotkov Solving PostgreSQL wicked problems 18 / 40
Solu on: row-level WAL Heap Index #1 Index #2 WAL ▶ Very compact. ▶ Apply can be parallelized. ▶ Suitable for mul master (row-level conflicts, not block-level). ▶ Recovery needs structurally consistent checkpoints. Alexander Korotkov Solving PostgreSQL wicked problems 18 / 40
Row-level WAL based mul master OrioleDB instance Storage WAL OrioleDB instance Storage WAL OrioleDB instance Storage WAL Raft replication Alexander Korotkov Solving PostgreSQL wicked problems 19 / 40
Copy-on-write checkpoints (1/4) 1 2 3 5 7 1 2 3 4 5 6 7 Disk Alexander Korotkov Solving PostgreSQL wicked problems 20 / 40
Copy-on-write checkpoints (2/4) 1 2 3 5 7* 1 2 3 4 5 6 7 Disk Alexander Korotkov Solving PostgreSQL wicked problems 21 / 40
Copy-on-write checkpoints (3/4) 1* 2 3* 5 7* 1 2 3 4 5 6 7 Disk 7* 3* 1* Alexander Korotkov Solving PostgreSQL wicked problems 22 / 40
Copy-on-write checkpoints (4/4) 1* 2 3* 5 7* 2 4 5 6 Disk 7* 3* 1* Alexander Korotkov Solving PostgreSQL wicked problems 23 / 40
What do we need from PostgreSQL extendability? Backgroud processes Backend Connection Parser Rewriter Planner Executor Autovacuum Background writer Checkpointer WAL writer PostgreSQL server File system Data files WAL files Log files OrioleDB extension OrioleDB data files OrioleDB undo files ...... File system ▶ Extended table AM. Alexander Korotkov Solving PostgreSQL wicked problems 24 / 40
What do we need from PostgreSQL extendability? Backgroud processes Backend Connection Parser Rewriter Planner Executor Autovacuum Background writer Checkpointer WAL writer PostgreSQL server File system Data files WAL files Log files OrioleDB extension OrioleDB data files OrioleDB undo files ...... File system ▶ Extended table AM. ▶ Custom toast handlers. Alexander Korotkov Solving PostgreSQL wicked problems 24 / 40
What do we need from PostgreSQL extendability? Backgroud processes Backend Connection Parser Rewriter Planner Executor Autovacuum Background writer Checkpointer WAL writer PostgreSQL server File system Data files WAL files Log files OrioleDB extension OrioleDB data files OrioleDB undo files ...... File system ▶ Extended table AM. ▶ Custom toast handlers. ▶ Custom row iden fiers. Alexander Korotkov Solving PostgreSQL wicked problems 24 / 40
What do we need from PostgreSQL extendability? Backgroud processes Backend Connection Parser Rewriter Planner Executor Autovacuum Background writer Checkpointer WAL writer PostgreSQL server File system Data files WAL files Log files OrioleDB extension OrioleDB data files OrioleDB undo files ...... File system ▶ Extended table AM. ▶ Custom toast handlers. ▶ Custom row iden fiers. ▶ Custom error cleanup. Alexander Korotkov Solving PostgreSQL wicked problems 24 / 40
What do we need from PostgreSQL extendability? Backgroud processes Backend Connection Parser Rewriter Planner Executor Autovacuum Background writer Checkpointer WAL writer PostgreSQL server File system Data files WAL files Log files OrioleDB extension OrioleDB data files OrioleDB undo files ...... File system ▶ Extended table AM. ▶ Custom toast handlers. ▶ Custom row iden fiers. ▶ Custom error cleanup. ▶ Recovery & checkpointer hooks. Alexander Korotkov Solving PostgreSQL wicked problems 24 / 40
What do we need from PostgreSQL extendability? Backgroud processes Backend Connection Parser Rewriter Planner Executor Autovacuum Background writer Checkpointer WAL writer PostgreSQL server File system Data files WAL files Log files OrioleDB extension OrioleDB data files OrioleDB undo files ...... File system ▶ Extended table AM. ▶ Custom toast handlers. ▶ Custom row iden fiers. ▶ Custom error cleanup. ▶ Recovery & checkpointer hooks. ▶ Snapshot hooks. Alexander Korotkov Solving PostgreSQL wicked problems 24 / 40
What do we need from PostgreSQL extendability? Backgroud processes Backend Connection Parser Rewriter Planner Executor Autovacuum Background writer Checkpointer WAL writer PostgreSQL server File system Data files WAL files Log files OrioleDB extension OrioleDB data files OrioleDB undo files ...... File system ▶ Extended table AM. ▶ Custom toast handlers. ▶ Custom row iden fiers. ▶ Custom error cleanup. ▶ Recovery & checkpointer hooks. ▶ Snapshot hooks. ▶ Some other miscellaneous hooks total 1K lines patch to PostgreSQL Core Alexander Korotkov Solving PostgreSQL wicked problems 24 / 40
OrioleDB = PostgreSQL redesign PostgreSQL Block-level WAL Row-level WAL Buffer mapping Direct page links Buffer locking Lock-less access Bloat-prone MVCC Undo log Cumbersome block-level WAL replication Raft-based multimaster replication of row- level WAL Alexander Korotkov Solving PostgreSQL wicked problems 25 / 40
OrioleDB’s answer to 10 wicked problems of PostgreSQL Problem name Solu on 1. Wraparound Na ve 64-bit transac on ids 2. Failover Will Probably Lose Data Mul master replica on 3. Inefficient Replica on That Spreads Corrup on Row-level replica on 4. MVCC Garbage Frequently Painful Non-persistent undo log 5. Process-Per-Connec on = Pain at Scale Migra on to mul thread model 6. Primary Key Index is a Space Hog Index-organized tables 7. Major Version Upgrades Can Require Down me Mul master + per-node upgrade 8. Somewhat Cumbersome Replica on Setup Simple setup of ra -based mul master 9. Ridiculous No-Planner-Hints Dogma In-core planner hints 10. No Block Compression Block-level compression * Scalability on modern hardware Alexander Korotkov Solving PostgreSQL wicked problems 26 / 40
Let’s do some benchmarks! 4 4 https://gist.github.com/akorotkov/f5e98ba5805c42ee18bf945b30cc3d67 Alexander Korotkov Solving PostgreSQL wicked problems 27 / 40
OrioleDB benchmark: read-only scalability 0 50 100 150 200 250 # Clients 0 200000 400000 600000 800000 TPS Read-only scalability test PostgreSQL vs OrioleDB 1 minute of pgbench script reading 9 random values of 100M PostgreSQL OrioleDB OrioleDB: 4X higher TPS! Alexander Korotkov Solving PostgreSQL wicked problems 28 / 40
OrioleDB benchmark: read-write scalability in-memory case 0 50 100 150 200 250 # Clients 0 100000 200000 300000 400000 TPS Read-write scalability test PostgreSQL vs OrioleDB 1 minute of pgbench TPC-B like transactions wrapped into stored procedure PostgreSQL OrioleDB OrioleDB: 3.5X higher TPS! Alexander Korotkov Solving PostgreSQL wicked problems 29 / 40
OrioleDB benchmark: read-write scalability external storage case 0 250 500 750 1000 1250 1500 1750 2000 # Clients 0 20000 40000 60000 80000 100000 120000 TPS pgbench -s 20000 -j $n -c $n -M prepared on odb-node02 mean of 3 3-minute runs with shared_buffers = 32GB(128GB), max_connections = 2500 pgsql-read-write orioledb-read-write orioledb-read-write-block-device OrioleDB: up to 50X higher TPS! Alexander Korotkov Solving PostgreSQL wicked problems 30 / 40
OrioleDB benchmark: read-write scalability Intel Optane persistent memory 0 250 500 750 1000 1250 1500 1750 2000 # Clients 0 25000 50000 75000 100000 125000 150000 175000 200000 TPS pgbench -s 20000 -j $n -c $n -M prepared -f read-write-proc.sql on node03 5-minute run with shared_buffers = 32GB, max_connections = 2500 pgsql orioledb-fsdax orioledb-devdax OrioleDB: up to 50X higher TPS! Alexander Korotkov Solving PostgreSQL wicked problems 31 / 40
OrioleDB benchmark: write-amplifica on & bloat test: CPU 400 600 800 1000 1200 1400 1600 seconds 0 100000 200000 300000 400000 500000 600000 700000 800000 TPS Troughtput PostgreSQL OrioleDB 400 600 800 1000 1200 1400 1600 seconds 0 20 40 60 80 100 Usage, % CPU usage PostgreSQL OrioleDB OrioleDB: 5X higher TPS! 2.3X less CPU/TPS! Alexander Korotkov Solving PostgreSQL wicked problems 32 / 40
OrioleDB benchmark: write-amplifica on & bloat test: IO 400 600 800 1000 1200 1400 1600 seconds 0 100000 200000 300000 400000 500000 600000 700000 800000 TPS Troughtput PostgreSQL OrioleDB 0 250 500 750 1000 1250 1500 1750 seconds 0 5000 10000 15000 20000 25000 30000 35000 IOPS IO load PostgreSQL OrioleDB OrioleDB: 5X higher TPS! 22X less IO/TPS! Alexander Korotkov Solving PostgreSQL wicked problems 33 / 40
OrioleDB benchmark: write-amplifica on & bloat test: space 400 600 800 1000 1200 1400 1600 seconds 0 10 20 30 40 50 60 70 80 GB Space used PostgreSQL OrioleDB OrioleDB: no bloat! Alexander Korotkov Solving PostgreSQL wicked problems 34 / 40
OrioleDB benchmark: taxi workload (1/3): read 0 500 1000 1500 2000 2500 3000 3500 seconds 0 25 50 75 100 125 150 175 IOPS Disk read PostgreSQL OrioleDB OrioleDB: 9X less read IOPS! Alexander Korotkov Solving PostgreSQL wicked problems 35 / 40
OrioleDB benchmark: taxi workload (2/3): write 0 500 1000 1500 2000 2500 3000 3500 seconds 0 50 100 150 200 250 300 350 IOPS Disk write PostgreSQL OrioleDB OrioleDB: 4.5X less write IOPS! Alexander Korotkov Solving PostgreSQL wicked problems 36 / 40
OrioleDB benchmark: taxi workload (3/3): space 0 500 1000 1500 2000 2500 3000 3500 seconds 0 5 10 15 20 25 30 35 40 GB Space used PostgreSQL OrioleDB OrioleDB: 8X less space usage! Alexander Korotkov Solving PostgreSQL wicked problems 37 / 40
OrioleDB = Solu on of wicked PostgreSQL problems + extraordinary performance Alexander Korotkov Solving PostgreSQL wicked problems 38 / 40
Roadmap ▶ Basic engine features 4 ▶ Table AM interface implementa on 4 ▶ Data compression 4 ▶ Undo log 4 ▶ TOAST support 4 ▶ Parallel row-level replica on 4 ▶ Par al and expression indexes 4 Ini al release ▶ GiST/GIN analogues Alexander Korotkov Solving PostgreSQL wicked problems 39 / 40
OrioleDB status ▶ Release is scheduled for December 1st 2021; ▶ https://github.com/orioledb/orioledb; ▶ If you need more explana on, don’t hesitate to make pull requests. Alexander Korotkov Solving PostgreSQL wicked problems 40 / 40