Concurrency Control in MongoDB 3.0

Jun 5, 2015Download as PPTX, PDF12 likes10,625 views

- MongoDB's concurrency control uses multiple-granularity locking at the instance, database, and collection level. This allows finer-grained locking than previous approaches. - The storage engine handles concurrency control at lower levels like the document level, using either MVCC or locking depending on the engine. WiredTiger uses MVCC while MMAPv1 uses locking at the collection level. - Intents signal the intention to access lower levels without acquiring locks upfront, improving concurrency compared to directly acquiring locks. The lock manager enforces the locking protocol and ensures consistency.

Concurrency Control in MongoDB 3.0
Kaloian Manassiev
Senior Engineer at MongoDB
kaloianm@mongodb.com
@kaloianm

2
Audience
• Operations engineers
• Application developers
• Third-party storage engine developers
• Anybody who is curious

4
What is concurrency control?
• Locking
• Data consistency
$100 + $100 = $200 (not $100 )
• MVCC

6
Collection Collection
Storage Engine API

7
What you will learn
• Top level concurrency control
–Instance, database and collection
• How top level cooperates with the
storage engine

8
What you will NOT learn
• WiredTiger internals
–Separate session on this

9
Talk outline
• MongoDB concurrency control
• Multiple-granularity locking
• WiredTiger vs MMAP V1
• Questions

10
MongoDB 2.0 (and before)
THE
TOP LOCK
R W
R OK NO
W NO NO

11
MongoDB 2.2
Lock per database
db1.coll.insert({a:1}) db4.coll.find({c:5})

13
TOP
db1.coll.insert({a:1}) db4.coll.find({c:5})
IX IS
X S

14
TOP
db.coll.insert({a:1}) db.coll.find({c:5})
IX IS
X S

15
Why intents?
• Get rid of the global lock?
• Use read (shared) lock?

16
TOP
db1.coll.insert({a:1}) db4.coll.find({c:5})
S S
X S

17
TOP
db1.coll.insert({a:1}) db.fsyncLock({lock:1})
S S
X

18
TOP
db1.coll.insert({a:1}) db.fsyncLock({lock:1})
IX S
X

19
TOP
db1.coll.insert({a:1}) db.fsyncLock({lock:1})
IX S
XS S S S

20
TOP
db.fsyncLock({lock:1})
S
S S S S
db1.coll.find({a:1})
IS
S

21
Intents
• “Intention” to access one or more
children of an item
–Compatible with other intents
–Need to acquire lock further down
–No overhead

22
Locks
• “Lock” an item for particular access
–Item being locked
–All items below it

23
Intents compatibility
IS IX S X
IS OK OK OK NO
IX OK OK NO NO
S OK NO OK NO
X NO NO NO NO

26
Lock Manager
• Ensures the locking protocol is
obeyed
• Very low overhead
• Testable

27
Lock Manager
• Improved fairness
• Support for locking policies
• Lock statistics gathering

28
TOP
db.coll1.update({a:1}) db.coll1.update({a:4})
IX IX
IX IX
IX IX
X X

30
Storage Engine API
TOP
Storage Engine API

31
TOP
db.coll1.update({a:1}) db.coll1.update({a:4})
IX IX
IX IX
IX IX
WiredTiger MVCC

32
TOP
db.coll1.update({a:1}) db.coll1.drop()
IX IX
IX X
IX
WiredTiger MVCC

33
How does MMAP V1 work?
• Supports collection concurrency
• Takes lock on the collection
–Instead of intent

34
TOP
db.coll1.update({a:1}) db.coll2.update({a:4})
IX IX
IX IX
X X
MMAP V1

35
TOP
db.coll1.update({a:1}) db.coll1.find({a:4})
IX IS
IX IS
X S
MMAP V1

36
WiredTiger
• Intents: Global, Database, Collection
• Document-level concurrency left to the
storage engine

37
MMAP V1
• Intents: Global, Database
• Locks: Collection

38
Conclusion
• Storage engines have direct control
over concurrency
–Completely decoupled from top-level
locking
–Enabled document-level control

39
Conclusion
• Retrofitted MMAP V1 to use multi-
granularity locks
–Support for collection-level locking
–Got rid of the global lock usage for
journaling

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This presentation discusses migrating data from other data stores to MongoDB Atlas. It begins by explaining why MongoDB and Atlas are good choices for data management. Several preparation steps are covered, including sizing the target Atlas cluster, increasing the source oplog, and testing connectivity. Live migration, mongomirror, and dump/restore options are presented for migrating between replicasets or sharded clusters. Post-migration steps like monitoring and backups are also discussed. Finally, migrating from other data stores like AWS DocumentDB, Azure CosmosDB, DynamoDB, and relational databases are briefly covered.

MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB

MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB

MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB

MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB

MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB

MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB

Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe. This talk covers: Common components of an IoT solution The challenges involved with managing time-series data in IoT applications Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance. How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.

MongoDB SoCal 2020: MongoDB Atlas Jump StartMongoDB

MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB

Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.

MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB

Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch". This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.

MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB

MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB

MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB

When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.

MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB

MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB

The document discusses guidelines for ordering fields in compound indexes to optimize query performance. It recommends the E-S-R approach: placing equality fields first, followed by sort fields, and range fields last. This allows indexes to leverage equality matches, provide non-blocking sorts, and minimize scanning. Examples show how indexes ordered by these guidelines can support queries more efficiently by narrowing the search bounds.

MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB

MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB

The document describes a methodology for data modeling with MongoDB. It begins by recognizing the differences between document and tabular databases, then outlines a three step methodology: 1) describe the workload by listing queries, 2) identify and model relationships between entities, and 3) apply relevant patterns when modeling for MongoDB. The document uses examples around modeling a coffee shop franchise to illustrate modeling approaches and techniques.

MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB

MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business. This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.

MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB

Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms. How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms? In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.

MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB

MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB

Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $. La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.