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![AGGREGATION - MEMORY CONSTRAINTS
➤ Results are subject to 16 MB
➤ Use $limit and $project
➤ 100MB limit per stage
➤ Use indexes
➤ db.orders.aggregate([], {allowDiskUse: true})](https://image.slidesharecdn.com/boosting-mongodb-performance-180522182906/85/Boosting-MongoDB-performance-33-320.jpg)
Uploaded byAlexei Panin
Boosting MongoDB performance
This document discusses various aspects of optimizing MongoDB performance, including hardware considerations, indexing strategies, and I/O operations. It highlights the importance of utilizing CPU resources efficiently, creating different types of indexes, and managing memory constraints during aggregation operations. Techniques for enhancing write performance and understanding query execution are also addressed.
Boosting MongoDB performance
- 1. BOOSTING MONGODB PERFORMANCE PaninAlexei, IT enthusiast
- 2. ➤ Facebook: https://www.facebook.com/panin.alexei ➤Twitter: https://twitter.com/AlexeiPanin ➤ Linkedin: https://www.linkedin.com/in/alexeipanin/ ➤ Email: panin.alexei@gmail.com
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- 5. CPU ( ALU +CU ) RAM / MEMORY I/O devices VON NEUMANN ARCHITECTURE
- 6. RANDOM ACCESS MEMORY ➤Aggregation ➤ Index traversal ➤ Write operations ➤ Query engine ➤ Connections RAM / MEMORY
- 7. CENTRAL PROCESSING UNIT CPU (ALU + CU ) ➤ Storage engine ➤ Page compression, data calculation, aggregation framework, map reduce ➤ Concurrency model ➤ By default MongoDB will use all available CPU cores for non blocking operations WRITES READS
- 8. CENTRAL PROCESSING UNIT- SEQUENTIAL OPERATIONS CPU ( ALU + CU ) ➤ db.users.update({“id”: 1 }, {$inc: {“score”: 1}}) ➤ db.users.update({“id”: 1 }, {$inc: {“score”: 1}}) ➤ db.users.update({“id”: 1 }, {$inc: {“score”: 1}}) ➤ db.users.update({“id”: 1 }, {$inc: {“score”: 1}}) ➤ db.users.update({“id”: 1 }, {$inc: {“score”: 1}}) ➤ db.users.update({“id”: 1 }, {$inc: {“score”: 1}}) WRITES READS
- 9. INPUT / OUTPUT ➤Higher IOPS - is better Type IOPS 7200 rpm SATA ~75 - 100 15000 rpm SAS SSD Intel X25-E ~5000 SSD Intel X25-M-GE ~8000 FUSION IO ~135 000 Violin Memory 6000 ~1 000 000
- 10. INPUT / OUTPUT- RECOMMENDED ARCHITECTURE http://www.10gen.com/presentations/mongodb-austin/2012/mongodb-on-amazon-ec2
- 11. INPUT / OUTPUT- NOT RECOMMENDED ARCHITECTURE http://www.10gen.com/presentations/webinar-best- practices-mongodb-aws
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- 13. MONGODB FILES >> mongod--dbpath ./wired-tiger-data/ Index data Collection data Lock file WiredTiger files
- 14. MONGODB FILES >> mongod--dbpath ./wired-tiger-data/ --directoryperdb --wiredTigerDirectoryForIndexes Index data Collection data Lock file WiredTiger files local database admin database
- 15. SINGLE FIELD INDEX >>db.users.createIndex({name: 1}) // ascending >> db.users.createIndex({surname: -1}) // descending >> mongoimport -d mongoperfdb -c items --drop ~/Desktop/mongo-data/assets/items.json
- 16. SINGLE FIELD INDEX >>db.users.createIndex({name: 1}) // ascending >> db.users.createIndex({surname: -1}) // descending >> mongoimport -d mongoperfdb -c items --drop ~/Desktop/mongo-data/assets/items.json
- 17. SINGLE FIELD INDEX- SORT >> use mongoperfdb >> db.items.dropIndexes() >> db.items.find({"ssn": /^880/}).sort({"ssn": 1}).explain("executionStats") >> db.items.createIndex({"ssn": 1}) >> db.items.find({"ssn": /^880/}).sort({"ssn": 1}).explain(“executionStats") >> db.items.find({"ssn": /^880/}).sort({"ssn": -1}).explain("executionStats")
- 18. INDEX PREFIX >> db.items.createIndex({"first_name":1, "last_name": 1, "job": 1}) // INDEX PREFIXES {“first_name”} {“first_name”, “last_name”}
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- 20. PARTIAL INDEX >> db.items.createIndex({ "first_name":1, "last_name": 1 }, {"partialFilterExpression": { "job": {"$gte": “M”} } })
- 21. SPARSE INDEX >> db.items.createIndex({ "first_name":1, "last_name": 1 }, {“sparse": true} })
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- 23. COLLATIONS >> db.items.createIndex({“first_name”: 1},{“colation”: “fr”}) >> db.items.find({“first_name”: “françois”}) >> db.items.find({“first_name”: “françois”}).collation({“locale”: “fr”})
- 24. BUILDING INDEXES Background indexForeground index currnetOp() | killOp()
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- 27. EQUALITY | SORT| RANGE
- 28. COVERED QUERIES db.items.dropIndexes() db.items.createIndex({"ssn": 1}) db.items.find({"ssn":{"$gte": "5"}}, {"ssn": 1, "_id": 0}).explain("executionStats")
- 29. REGEX PERFORMANCE db.items.dropIndexes() db.items.createIndex({“first_name”: 1}) db.items.find({“first_name”:/alexei/})// COLLSCAN db.items.find({“first_name”:/^alexei/}) // IXSCAN
- 30. WRITE PERF IMPACTFACTORS Write concern Index overhead
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- 33. AGGREGATION - MEMORYCONSTRAINTS ➤ Results are subject to 16 MB ➤ Use $limit and $project ➤ 100MB limit per stage ➤ Use indexes ➤ db.orders.aggregate([], {allowDiskUse: true})
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