![# M D B l o c a l
{ "name": "Daniel Coupal",
"jobs_at_MongoDB": [
{ "job": "Senior Curriculum Engineer",
"from": new Date("2016-11") },
{ "job": "Senior Technical Service Engineer",
"from": new Date("2013-11") }
],
"previous_jobs": [
"Consultant",
"Developer",
"Manager Quality & Tools Team",
"Manager Software Team",
"Tools Developer"
],
"likes": [ "food", "beers", "movies", "MongoDB" ]
}
Who Am I?](https://image.slidesharecdn.com/edited-advanced-schema-design-51d306dd-a2d6-4716-ba41-08e9c1c0f62e-322959707-171018133322/85/Advanced-Schema-Design-Patterns-2-320.jpg)
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Advanced Schema Design Patterns
The document outlines advanced schema design patterns for MongoDB, emphasizing the importance of a common methodology and vocabulary for effective schema modeling. It discusses various issues such as performance, scalability, and simplicity, providing solutions through design patterns like attribute, subset, computed, approximation, and schema versioning. The presentation encourages utilizing these patterns to enhance database performance and reduce costs, while introducing case studies for practical application.
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Advanced Schema Design Patterns
- 1. O C TO B E R 1 2 , 2 0 1 7 | B E S P O K E | S A N F R A N C I S C O # M D B l o c a l Advanced Schema Design Patterns
- 2. # M DB l o c a l { "name": "Daniel Coupal", "jobs_at_MongoDB": [ { "job": "Senior Curriculum Engineer", "from": new Date("2016-11") }, { "job": "Senior Technical Service Engineer", "from": new Date("2013-11") } ], "previous_jobs": [ "Consultant", "Developer", "Manager Quality & Tools Team", "Manager Software Team", "Tools Developer" ], "likes": [ "food", "beers", "movies", "MongoDB" ] } Who Am I?
- 3. # M DB l o c a l The "Gang of Four": A design pattern systematically names, explains, and evaluates an important and recurring design in object-oriented systems MongoDB systems can also be built using its own patterns PATTERN Pattern
- 4. # M DB l o c a l • Enable teams to use a common methodology and vocabulary when designing schemas for MongoDB • Giving you the ability to model schemas using building blocks • Less art and more methodology Why this Talk?
- 5. # M DB l o c a l Ensure: • Good performance • Scalability despite constraints ➡ • Hardware • RAM faster than Disk • Disk cheaper than RAM • Network latency • Reduce costs $$$ • Database Server • Maximum size for a document • Atomicity of a write • Data set • Size of data Why do we Create Models?
- 6. # M DB l o c a l •Don’t over-design! •Design for: •Performance •Scalability •Simplicity However …
- 7. # M DB l o c a l WMDB - World Movie Database Any events, characters and entities depicted in this presentation are fictional. Any resemblance or similarity to reality is entirely coincidental
- 8. # M DB l o c a l WMDB - World Movie Database First iteration 3 collections: A. movies B. moviegoers C. screenings
- 9. # M DB l o c a l Our mission, should we decide to accept it, is to fix this solution, so it can perform well and scale. As always, should I or anyone in the audience do it without training, WMDB will disavow any knowledge of our actions. This tape will self-destruct in five seconds. Good luck! Mission Possible
- 10. # M DB l o c a l Categories of Patterns • Frequency of Access • Subset ✓ • Approximation ✓ • Grouping • Computed ✓ • Overflow • Bucket • Representation • Attribute ✓ • Schema Versioning ✓ • Document Versioning • Tree • Pre-Allocation
- 11. # M DB l o c a l { title: "Moonlight", ... release_USA: "2016/09/02", release_Mexico: "2017/01/27", release_France: "2017/02/01", release_Festival_Mill_Valley: "2017/10/10" } Would need the following indexes: { release_USA: 1 } { release_Mexico: 1 } { release_France: 1 } ... { release_Festival_Mill_Valley: 1 } ... Issue #1: Big Documents, Many Fields and Many Indexes
- 12. # M DB l o c a l Pattern #1: Attribute { title: "Moonlight", ... release_USA: "2016/09/02", release_Mexico: "2017/01/27", release_France: "2017/02/01", release_Festival_Mill_Valley: "2017/10/10" }
- 13. # M DB l o c a l Problem: • Lots of similar fields • Common characteristic to search across those fields together • Fields present in only a small subset of documents Use cases: • Product attributes like ‘color’, ‘size’, ‘dimensions’, ... • Release dates of a movie in different countries, festivals Attribute Pattern
- 14. # M DB l o c a l Solution: • Field pairs in an array Benefits: • Allow for non deterministic list of attributes • Easy to index { "releases.location": 1, "releases.date": 1 } • Easy to extend with a qualifier, for example: { descriptor: "price", qualifier: "euros", value: Decimal(100.00) } Attribute Pattern - Solution
- 15. # M DB l o c a l Possible solutions: A. Reduce the size of your working set B. Add more RAM per machine C. Start sharding or add more shards Issue #2: Working Set doesn’t fit in RAM
- 16. # M DB l o c a l WMDB - World Movie Database First iteration 3 collections: A. movies B. moviegoers C. screenings
- 17. # M DB l o c a l In this example, we can: • Limit the list of actors and crew to 20 • Limit the embedded reviews to the top 20 • … Pattern #2: Subset
- 18. # M DB l o c a l Problem: • There is a 1-N or N-N relationship, and only few documents from need to be shown always • Only infrequently do you need to pull all of the depending documents Use cases: • Main actors of a movie • List of reviews or comments Subset Pattern
- 19. # M DB l o c a l Solution: • Keep duplicates of a small subset of fields in the main collection Benefits: • Allows for fast data retrieval and a reduced working set size • One query brings all the information needed for the "main page" Subset Pattern - Solution
- 21. # M DB l o c a l • How duplication is handled A. Update both source and target in real time B. Update target from source at regular intervals. Examples: • Most popular items => update nightly • Revenues from a movie => update every hour • Last 10 reviews => update hourly? daily? Aspect of Patterns: Consistency
- 22. # M DB l o c a l Issue #3: Lot of CPU Usage
- 23. # M DB l o c a l { title: "Your Name", ... viewings: 5,000 viewers: 385,000 revenues: 5,074,800 } Issue #3: ..caused by repeated calculations
- 24. # M DB l o c a l For example: • Apply a sum, count, ... • rollup data by minute, hour, day • As long as you don’t mess with your source, you can recreate the rollups Pattern #3: Computed
- 25. # M DB l o c a l Problem: • There is data that needs to be computed • The same calculations would happen over and over • Reads outnumber writes: • example: 1K writes per hour vs 1M read per hour Use cases: • Have revenues per movie showing, want to display sums • Time series data, Event Sourcing Computed Pattern
- 26. # M DB l o c a l Solution: • Apply a computation or operation on data and store the result Benefits: • Avoid re-computing the same thing over and over • Replaces a view Computed Pattern - Solution
- 27. # M DB l o c a l Issue #4: Lots of Writes Web page counters Updates on movie data Screenings Other
- 28. # M DB l o c a l Issue #4: … for non critical data
- 29. # M DB l o c a l • Only increment once in X iterations • Increment by X Pattern #4: Approximation
- 30. # M DB l o c a l Problem: • Data is difficult to calculate correctly • May be too expensive to update the document every time to keep an exact count • No one gives a damn if the number is exact Use cases: • Population of a country • Web site visits Approximation Pattern
- 31. # M DB l o c a l Solution: • Fewer stronger writes Benefits: • Less writes, reducing contention on some documents Approximation Pattern – Solution
- 32. # M DB l o c a l • Keeping track of the schema version of a document Issue #5: Need to change the list of fields in the documents
- 33. # M DB l o c a l Add a field to track the schema version number, per document Does not have to exist for version 1 Pattern #5: Schema Versioning
- 34. # M DB l o c a l Problem: • Updating the schema of a database is: • Not atomic • Long operation • May not want to update all documents, only do it on updates Use cases: • Practically any database that will go to production Schema Versioning Pattern
- 35. # M DB l o c a l Solution: • Have a field keeping track of the schema version Benefits: • Don't need to update all the documents at once • May not have to update documents until their next modification Schema Versioning Pattern – Solution
- 36. # M DB l o c a l • Bucket • grouping documents together, to have less documents • Document Versioning • tracking of content changes in a document • Outlier • Avoid few documents drive the design, and impact performance for all • Tree(s) • Pre-allocation Other Patterns
- 37.
- 38. # M DB l o c a l • Simple grouping from tables to collections is not optimal • Learn a common vocabulary for designing schemas with MongoDB • Use patterns as "plug-and-play" for your future designs • Attribute • Subset • Computed • Approximation • Schema Versioning Take Aways
- 39. # M DB l o c a l A full design example for a given problem: • E-commerce site • Contents Management System • Social Networking • Single view • … References for complete Solutions
- 40. # M DB l o c a l • More patterns in a follow up to this presentation • MongoDB in-person training courses on Schema Design • Upcoming Online course at MongoDB University: • https://university.mongodb.com • M220 Data Modeling How Can I Learn More About Schema Design?
- 41. # M DB l o c a l daniel.coupal@mongodb.com Thank You for using MongoDB!
Editor's Notes
- #2 Welcome [Remember] Beware of transitions, keep them smooth [TODOs] Add the page numbers Drawing of a working set Consider removing ":" in the slide titles Consider changing "revenues" => revenue, in few slides More on the value and use cases for each pattern
- #3 Previous Jobs, Order of likes, =>Gang of Four I like Food, Beers and Movies … and MongoDB. My inspiration for this talk comes from the "Gang of Four". How many of you are familiar with the "Gang of Four"?
- #4 Building blocks, Some patterns, => Same for MongoDB Basically the ones who wrote this book on "Design Patterns" GOF are Erich Gamma, Richard Helm, Ralph Johnson and John Vlissides https://en.wikipedia.org/wiki/Design_Patterns Key words are "Elements of Reusable Software" Assemble their experience on designing and implementing software over the years They found that a lot of the solutions were sharing some "patterns" Examples of patterns from "Design Patterns" Types: Creational (5), Structural (7), Behavioral (11) Singleton (restrict the creation to a single object for a given class) Observer (number of objects to see an event) Command (user operation) Decorator (embellishing a UI element) Memento (ability to restore an object to a previous state) … So, they went and made a catalog of those "patterns". The idea is enable people who write software to share a common language and have building blocks for solutions.
- #5 10 Years, Vocabulary, Building Blocks, "Art", => Example We use that contents in our internal trainings, however is it the first time we are presenting it at a conference, well… including the "data modeling" workshop we ran yesterday. The goal is not to teach you about doing schema design. I am expecting you to either have done some with MongoDB or with a Relational Database My goal is help you formalize the process of creating schemas for MongoDB, help you work in team by sharing visuals, vocabulary
- #6 Performance & scalability, "air" Before we get going, let's just answer why we create models. In a perfect world, you don't really have to model. I mean if everything is super fast and resources are abundant, you really don't care where and how data is stored Every day I get up I don't make plans on how I will breathe air. However if you go to space or under water, you will need a "design" that will let you get the amount of air you need.
- #7 Design is optional, cost of developer, 5 or 10 shards? If performance is not an issue, meaning you have resources to spare, then you are likely to model for simplicity. The reason is that software engineers are very expensive. You may not think so, but your manager does. If you need to shard the database, it is likely that performance is very important Why using 10 shards, if you can reduce the number of operations (reads and writes) by 2 and be able to do the same with 5 shards?
- #8 Entities In order to illustrate this talk, let's assume there is a site called the "World Movie Database". This site is so popular that everyone goes there on Thursdays before the release of new movies and it crashes the site. Then some people tried to migrate the site to a NoSQL database, MongoDB obviously.
- #9 Collections, grouping not optimal, =>accept challenge This is the first try of trying to move the schema from Relational to MongoDB. There are 3 collections: movies, moviegoers and screenings. Simply grouping entities into collections is not optimal. The solution using this design did not perform much better than the previous one. This is still normalized. When you remove this restriction, duplication is fine, 1-1 relationships are fine. You open the door to some important transformations. Those will be our patterns. [NOTE] Use "Sync Visibility" once you activate the color layer to also see it in the PNG file.
- #10 Perform & Scale, without training, disavow Our goal, no need to say, is to fix this website before it gets the same fate as this tape recorder.
- #11 GoF, top 5 patterns in order, We will use patterns, like the Gang of Four. Most patterns can be grouped in 3 categories. We will cover those patterns identified with check marks in this presentation. Also, I will cover the patterns in order of importance, or so. For the other ones, I will refer you to the slides of this presentation and subsequent content we will have on the subject.
- #12 How do I search on movies being released on a given date in the USA? The same would apply to products you could see on E-commerce site. For example, clothes may have a size that is expressed as S, M, L, while for some other products like a laptop, size would be something like 13", 15"
- #13 If you noticed from my personal info, I did use that pattern. That allowed me to list my jobs at MongoDB and associate them with a given date.
- #14 Inventory of things to insure Polymorphic entities Vehicles: submarine, car
- #15 "Adding a qualifier on the attribute" may be "currency"
- #16 Working set, imagine no more RAM With everyone pounding on the WMDB site, it was observed that the working set does not fit in memory. What can you do? Looking at the design we see that we are putting all the actors and all reviews for a given movie in the main document [TODO] Add a drawing showing what the working set is
- #17 Collections, grouping not optimal, =>accept challenge This is the first try of trying to move the schema from Relational to MongoDB. There are 3 collections: movies, moviegoers and screenings. Simply grouping entities into collections is not optimal. The solution using this design did not perform much better than the previous one. This is still normalized. When you remove this restriction, duplication is fine, 1-1 relationships are fine. You open the door to some important transformations. Those will be our patterns. [NOTE] Use "Sync Visibility" once you activate the color layer to also see it in the PNG file.
- #18 The collection "castandcrew" contains all the actors, but also the producers, costume makers, stunts, etc. For this pattern to be worth it, it has to have a fair amount of information left aside.
- #19 Top level information for a first page If this is slow, you may not keep your users on the site You want them to validate that this is what they want, then dig for more if needed
- #21 Let's take a pause there. Don't go get popcorn, not yet, this is just an intermission from our pattern list. [TODO] make this "intermission" more appealing
- #22 Let’s pause from our pattern list, and let’s examine a characteristic or aspect of some patterns.
- #23 As you may guess, people pay attention to the popularity of the movies. So, metrics like "revenues" and "viewers" are really important. In the current design, those numbers are calculated every time the page of a movie is displayed. Let’s calculate those numbers once in a while and stick the results on the page instead.
- #24 As you may guess, people pay attention to the popularity of the movies. So, metrics like "revenues" and "viewers" are really important. In the current design, those numbers are calculated every time the page of a movie is displayed. Let’s calculate those numbers once in a while and stick the results on the page instead.
- #25 Also refer to "Rolled up" as CQRS - Command Query Responsibility Segregation According to Bryan, that sounds good at a Party.
- #28 Another thing that was observed with the current design is that trying to keep track of all page views of the site resulted in very poor performance. That was seen for both MMAPv1 and WT. In MMAPv1, you get a lot of threads looking for the write lock. While with WT, you get a lot of write conflicts that need to be retried. One solution is to record "good enough" numbers. Well no one cares that the count is 100 millions or 100 millions and few. What is the tolerance level here? Let’s assume 1000. In this case, we will let the application update the page views by 1000, however only 1/1000th of the time. Statistically, we should get a result very close to the exact count, however doing only 1/1000th of the writes. If you make the parallel to a movie, we never see a movie as a continuous image, the movie is made by displaying 24 static images per second, however this is enough to our eyes to not see the discontinuties. How do you do that? Let’s have the application run a (X mod 1000) operation, where X is a random number. If the result is 0, let’s update the counter by 1000.
- #29 Another thing that was observed with the current design is that trying to keep track of all page views of the site resulted in very poor performance. That was seen for both MMAPv1 and WT. In MMAPv1, you get a lot of threads looking for the write lock. While with WT, you get a lot of write conflicts that need to be retried. One solution is to record "good enough" numbers. Well no one cares that the count is 100 millions or 100 millions and few. What is the tolerance level here? Let’s assume 1000. In this case, we will let the application update the page views by 1000, however only 1/1000th of the time. Statistically, we should get a result very close to the exact count, however doing only 1/1000th of the writes. If you make the parallel to a movie, we never see a movie as a continuous image, the movie is made by displaying 24 static images per second, however this is enough to our eyes to not see the discontinuties. How do you do that? Let’s have the application run a (X mod 1000) operation, where X is a random number. If the result is 0, let’s update the counter by 1000.
- #30 You can have a counter. Once you reach the count, you do the write. Or you can use a random generator and when you get a specific value, you do the write. As you guess, this simple pattern is also applicable to Relational databases. … it is just that NoSQL people have more tricks to handle performance bottlenecks.
- #33 Let's face it configuration management and database usually don't work well together. Database tend to keep the "latest" state of your data, while "CM" systems remember everything. Those of you who checked in stupid mistakes in Git, ClearCase, etc know what I am taking about. For this pattern, we are keeping track of the shape of the document. We are not addressing keeping track of the different contents of the document it self. This other case is solved by the Document Versioning pattern.
- #34 Instead of using a "version" field, we could discover the version number based on fields
- #35 - Few million references would not even fit into an embedded array. And if it did, you would not want to construct a query by passing a million values to the $in operator.
- #37 We touch a little bit the bucket pattern when we looked at the outlier one. The bucket pattern let you group X sub-documents into one document. When the bucket is full, you create another one. Pre-allocation will be the case where you pre-create an array of cells to have the reads and writes easily access the elements. This is a very important pattern if you are using MMAPv1, as continuously growing an array can have a negative effect. With Wired Tiger it is not as crucial, however may make the code in the application simpler. As for Trees are commonly represented by either having one node per document, where you can list the parent, the children, the ancestors, or a combination of those
- #38 [TODO] I need another title! Elliot and Dev went to the future to see if there are still people using relational databases there, so we can work on the missing features in our next release. I think they are looking at their watch to see if it is time to come back… or wait, maybe they want me to hurry up, so I will wrap up the presentation… We did use a fictional site, however all the patterns we used would also apply to "Internet of Things", "Single View", "E-commerce" solutions.
- #39 10 years, future data big or not square, becoming an expert MongoDB celebrates 10 years … very soon. We are able to identify patterns because we have seen a lot of models with MongoDB over those first 10 years. Those are "plug-and-play" elements that let you go faster in your designs. We do believe MongoDB has a bright future. Most data that could be put in a Relational Database is already there. We are left with: Data this is "not square", meaning it does not fit well in square tables. Large datasets We believe the document model and the scalability of MongoDB are prime to store that data Ensure you are ready for the future by becoming an expert on MongoDB and how to model for it
- #40 My goal was to introduce you to patterns, however if you want more complete solutions to common problems, there are few good books out there. Let me point you to those 2: The Little Mongo DB Schema Design Book Paperback, by Christian Kvalheim MongoDB Applied Design Patterns, by Rick Copeland
- #41 I am leaving you with where you can find more information about schema design M220 is likely to be available in Q4 2017
- #42 Thanks you for attending my presentation, and this conference, but above all: Thank you for using MongoDB!