CONCURRENCY PATTERNS WITH MONGODB Yann Cluchey CTO @ Cogenta
• Real-time retail intelligence • Gather products and prices from web • MongoDB in production • Millions of updates per day, 3K/s peak • Data in SQL, Mongo, ElasticSearch
Concurrency Patterns: Why? • MongoDB: atomic updates, no transactions • Need to ensure consistency & correctness • What are my options with Mongo? • Shortcuts • Different approaches
Concurrency Control Strategies • Pessimistic • Suited for frequent conflicts • http://bit.ly/two-phase-commits • Optimistic • Efficient when conflicts are rare • http://bit.ly/isolate-sequence • Multi-version • All versions stored, client resolves conflict • e.g. CouchDb
Optimistic Concurrency Control (OCC) • No locks • Prevent dirty writes • Uses timestamp or a revision number • Client checks & replays transaction
Example Original { _id: 23, comment: “The quick brown fox…” } Edit 1 { _id: 23, comment: “The quick brown fox prefers SQL” } Edit 2 { _id: 23, comment: “The quick brown fox prefers MongoDB” }
Example Edit 1 db.comments.update( { _id: 23 }, { _id: 23, comment: “The quick brown fox prefers SQL” }) Edit 2 db.comments.update( { _id: 23 }, { _id: 23, comment: “The quick brown fox prefers MongoDB” }) Outcome: One update is lost, other might be wrong
OCC Example Original { _id: 23, rev: 1, comment: “The quick brown fox…” } Update a specific revision (edit 1) db.comments.update( { _id: 23, rev: 1 }, { _id: 23, rev: 2, comment: “The quick brown fox prefers SQL” })
OCC Example Edit 2 db.comments.update( { _id: 23, rev: 1 }, { _id: 23, rev: 2, comment: “The quick brown fox prefers MongoDB” }) ..fails { updatedExisting: false, n: 0, err: null, ok: 1 } • Caveat: Only works if all clients follow convention
Update Operators in Mongo • Avoid full document replacement by using operators • Powerful operators such as $inc, $set, $push • Many operators can be grouped into single atomic update • More efficient (data over wire, parsing, etc.) • Use as much as possible • http://bit.ly/update-operators
Still Need OCC? A hit counter { _id: 1, hits: 5040 } Edit 1 db.stats.update({ _id: 1 }, { $set: { hits: 5045 } }) Edit 2 db.stats.update({ _id: 1 }, { $set: { hits: 5055 } })
Still Need OCC? Edit 1 db.stats.update({ _id: 1 }, { $inc: { hits: 5 } }) Edit 2 db.stats.update({ _id: 1 }, { $inc: { hits: 10 } }) • Sequence of updates might vary • Outcome always the same • But what if sequence is important?
Still Need OCC? • Operators can offset need for concurrency control • Support for complex atomic manipulation • Depends on use case • You’ll need it for • Opaque changes (e.g. text) • Complex update logic in app domain (e.g. changing a value affects some calculated fields) • Sequence is important and can’t be inferred
Update Commands • Update • Specify query to match one or more documents • Use { multi: true } to update multiple documents • Must call Find() separately if you want a copy of the doc • FindAndModify • Update single document only • Find + Update in single hit (atomic) • Returns the doc before or after update • Whole doc or subset • Upsert (update or insert) • Important feature. Works with OCC..?
Consistent Update Example • Have a customer document • Want to set the LastOrderValue and return the previous value db.customers.findAndModify({ query: { _id: 16, rev: 45 }, update: { $set: { lastOrderValue: 699 }, $inc: { rev: 1 } }, new: false })
Consistent Update Example • Customer has since been updated, or doesn’t exist • Client should replay null • Intended version of customer successfully updated • Original version is returned { _id: 16, rev: 45, lastOrderValue: 145 } • Useful if client has got partial information and needs the full document • A separate Find() could introduce inconsistency
Independent Update with Upsert • Keep stats about customers • Want to increment NumOrders and return new total • Customer document might not be there • Independent operation still needs protection db.customerStats.findAndModify({ query: { _id: 16 }, update: { $inc: { numOrders: 1, rev: 1 }, $setOnInsert: { name: “Yann” } }, new: true, upsert: true })
Independent Update with Upsert • First run, document is created { _id: 16, numOrders: 1, rev: 1, name: “Yann” } • Second run, document is updated { _id: 16, numOrders: 2, rev: 2, name: “Yann” }
Subdocuments • Common scenario • e.g. Customer and Orders in single document • Clients like having everything • Powerful operators for matching and updating subdocuments • $elemMatch, $, $addToSet, $push • Alternatives to “Fat” documents; • Client-side joins • Aggregation • MapReduce
Currency Control and Subdocuments • Patterns described here still work, but might be impractical • Docs are large • More collisions • Solve with scale?
Subdocument Example • Customer document contains orders • Want to independently update orders • Correct order #471 value to £260 { _id: 16, rev: 20, name: “Yann”, orders: { “471”: { id: 471, value: 250, rev: 4 } } }
Subdocument Example db.customers.findAndModify({ query: { “orders.471.rev”: { $lte: 4 } }, update: { $set: { “orders.471.value”: 260 }, $inc: { rev: 1, “orders.471.rev”: 1 }, $setOnInsert: { name: “Yann”, “orders.471.id”: 471 } }, new: true, upsert: true })
Subdocument Example • First run, order updated successfully • Could create if not exists { _id: 16, rev: 21, name: “Yann”, orders: { “471”: { id: 471, value: 260, rev: 5 } } }
Subdocument Example • Second conflicting run • Query didn’t match revision, duplicate document created { _id: ObjectId("533bf88a50dbb55a8a9b9128"), rev: 1, name: “Yann”, orders: { “471”: { id: 471, value: 260, rev: 1 } } }
Subdocument Example • Solve with unique index (good idea anyway) db.customers.ensureIndex( { "orders.id" : 1 }, { "name" : "orderids", "unique" : true })
Subdocument Example Client can handle findAndModify result accordingly; • Successful update { updatedExisting: true } • New document created { updatedExisting: false, n: 1 } • Conflict, need to replay { errmsg: “exception: E11000 duplicate key error index: db.customers.$orderids dup key…” }
Final Words • Don’t forget deletes • Gotchas about subdocument structure orders: [ { id: 471 }, … ] orders: { “471”: { }, … } orders: { “471”: { id: 471 }, … } • Coming in 2.6.0 stable $setOnInsert: { _id: .. } • Sharding..?

Concurrency Patterns with MongoDB

  • 1.
  • 2.
    • Real-time retailintelligence • Gather products and prices from web • MongoDB in production • Millions of updates per day, 3K/s peak • Data in SQL, Mongo, ElasticSearch
  • 3.
    Concurrency Patterns: Why? •MongoDB: atomic updates, no transactions • Need to ensure consistency & correctness • What are my options with Mongo? • Shortcuts • Different approaches
  • 4.
    Concurrency Control Strategies •Pessimistic • Suited for frequent conflicts • http://bit.ly/two-phase-commits • Optimistic • Efficient when conflicts are rare • http://bit.ly/isolate-sequence • Multi-version • All versions stored, client resolves conflict • e.g. CouchDb
  • 5.
    Optimistic Concurrency Control(OCC) • No locks • Prevent dirty writes • Uses timestamp or a revision number • Client checks & replays transaction
  • 6.
    Example Original { _id: 23,comment: “The quick brown fox…” } Edit 1 { _id: 23, comment: “The quick brown fox prefers SQL” } Edit 2 { _id: 23, comment: “The quick brown fox prefers MongoDB” }
  • 7.
    Example Edit 1 db.comments.update( { _id:23 }, { _id: 23, comment: “The quick brown fox prefers SQL” }) Edit 2 db.comments.update( { _id: 23 }, { _id: 23, comment: “The quick brown fox prefers MongoDB” }) Outcome: One update is lost, other might be wrong
  • 8.
    OCC Example Original { _id:23, rev: 1, comment: “The quick brown fox…” } Update a specific revision (edit 1) db.comments.update( { _id: 23, rev: 1 }, { _id: 23, rev: 2, comment: “The quick brown fox prefers SQL” })
  • 9.
    OCC Example Edit 2 db.comments.update( {_id: 23, rev: 1 }, { _id: 23, rev: 2, comment: “The quick brown fox prefers MongoDB” }) ..fails { updatedExisting: false, n: 0, err: null, ok: 1 } • Caveat: Only works if all clients follow convention
  • 10.
    Update Operators inMongo • Avoid full document replacement by using operators • Powerful operators such as $inc, $set, $push • Many operators can be grouped into single atomic update • More efficient (data over wire, parsing, etc.) • Use as much as possible • http://bit.ly/update-operators
  • 11.
    Still Need OCC? Ahit counter { _id: 1, hits: 5040 } Edit 1 db.stats.update({ _id: 1 }, { $set: { hits: 5045 } }) Edit 2 db.stats.update({ _id: 1 }, { $set: { hits: 5055 } })
  • 12.
    Still Need OCC? Edit1 db.stats.update({ _id: 1 }, { $inc: { hits: 5 } }) Edit 2 db.stats.update({ _id: 1 }, { $inc: { hits: 10 } }) • Sequence of updates might vary • Outcome always the same • But what if sequence is important?
  • 13.
    Still Need OCC? •Operators can offset need for concurrency control • Support for complex atomic manipulation • Depends on use case • You’ll need it for • Opaque changes (e.g. text) • Complex update logic in app domain (e.g. changing a value affects some calculated fields) • Sequence is important and can’t be inferred
  • 14.
    Update Commands • Update •Specify query to match one or more documents • Use { multi: true } to update multiple documents • Must call Find() separately if you want a copy of the doc • FindAndModify • Update single document only • Find + Update in single hit (atomic) • Returns the doc before or after update • Whole doc or subset • Upsert (update or insert) • Important feature. Works with OCC..?
  • 15.
    Consistent Update Example •Have a customer document • Want to set the LastOrderValue and return the previous value db.customers.findAndModify({ query: { _id: 16, rev: 45 }, update: { $set: { lastOrderValue: 699 }, $inc: { rev: 1 } }, new: false })
  • 16.
    Consistent Update Example •Customer has since been updated, or doesn’t exist • Client should replay null • Intended version of customer successfully updated • Original version is returned { _id: 16, rev: 45, lastOrderValue: 145 } • Useful if client has got partial information and needs the full document • A separate Find() could introduce inconsistency
  • 17.
    Independent Update withUpsert • Keep stats about customers • Want to increment NumOrders and return new total • Customer document might not be there • Independent operation still needs protection db.customerStats.findAndModify({ query: { _id: 16 }, update: { $inc: { numOrders: 1, rev: 1 }, $setOnInsert: { name: “Yann” } }, new: true, upsert: true })
  • 18.
    Independent Update withUpsert • First run, document is created { _id: 16, numOrders: 1, rev: 1, name: “Yann” } • Second run, document is updated { _id: 16, numOrders: 2, rev: 2, name: “Yann” }
  • 19.
    Subdocuments • Common scenario •e.g. Customer and Orders in single document • Clients like having everything • Powerful operators for matching and updating subdocuments • $elemMatch, $, $addToSet, $push • Alternatives to “Fat” documents; • Client-side joins • Aggregation • MapReduce
  • 20.
    Currency Control andSubdocuments • Patterns described here still work, but might be impractical • Docs are large • More collisions • Solve with scale?
  • 21.
    Subdocument Example • Customerdocument contains orders • Want to independently update orders • Correct order #471 value to £260 { _id: 16, rev: 20, name: “Yann”, orders: { “471”: { id: 471, value: 250, rev: 4 } } }
  • 22.
    Subdocument Example db.customers.findAndModify({ query: {“orders.471.rev”: { $lte: 4 } }, update: { $set: { “orders.471.value”: 260 }, $inc: { rev: 1, “orders.471.rev”: 1 }, $setOnInsert: { name: “Yann”, “orders.471.id”: 471 } }, new: true, upsert: true })
  • 23.
    Subdocument Example • Firstrun, order updated successfully • Could create if not exists { _id: 16, rev: 21, name: “Yann”, orders: { “471”: { id: 471, value: 260, rev: 5 } } }
  • 24.
    Subdocument Example • Secondconflicting run • Query didn’t match revision, duplicate document created { _id: ObjectId("533bf88a50dbb55a8a9b9128"), rev: 1, name: “Yann”, orders: { “471”: { id: 471, value: 260, rev: 1 } } }
  • 25.
    Subdocument Example • Solvewith unique index (good idea anyway) db.customers.ensureIndex( { "orders.id" : 1 }, { "name" : "orderids", "unique" : true })
  • 26.
    Subdocument Example Client canhandle findAndModify result accordingly; • Successful update { updatedExisting: true } • New document created { updatedExisting: false, n: 1 } • Conflict, need to replay { errmsg: “exception: E11000 duplicate key error index: db.customers.$orderids dup key…” }
  • 27.
    Final Words • Don’tforget deletes • Gotchas about subdocument structure orders: [ { id: 471 }, … ] orders: { “471”: { }, … } orders: { “471”: { id: 471 }, … } • Coming in 2.6.0 stable $setOnInsert: { _id: .. } • Sharding..?

Editor's Notes

  • #3 4xSQL, 1xMongo, 5xElastic
  • #4 No transactions.Different databases have different features.Cheating is fun. How can we avoid problems entirely.Efficiency is key. Understand what’s achievable in a single update
  • #5 All doable in MongoDBOptimistic usually the best option for typical mongodb projectHow to roll yo
  • #7 Two users simultaneously editing
  • #8 Not excitingly clever, or efficient..?
  • #9 Not excitingly clever, or efficient..?
  • #10 Not excitingly clever, or efficient..?
  • #11 They are your friends, go play wit
  • #12 Two users simultaneously editing
  • #13 Bank balance!
  • #20 Not going to get in to that…
  • #21 Not going to get in to that…
  • #22 Data is mastered elsewhere