Kyle Banker
MongoDB Rules
Summarized using AI
MongoDB Rules
by Kyle BankerIn the talk "MongoDB Rules" presented by Kyle Banker at MountainWest RubyConf 2010, the speaker introduces MongoDB, an open-source, document-based NoSQL database. Banker's focus is on providing attendees with practical rules for effectively using MongoDB, blending introductory knowledge with actionable insights.
Key Points Discussed:
- Understanding MongoDB: Participants learn what MongoDB is and its operational advantages, particularly in terms of performance and scalability. Banker emphasizes not just the technical aspects but also the document-oriented nature of MongoDB that simplifies data handling.
- Rules to Use MongoDB Effectively:
- Rule 1: Get to know the real MongoDB by utilizing its JavaScript shell, which allows users to interact directly with MongoDB without relying on object mappers that may obscure its functionality.
- Rule 2: Prefer using ObjectIDs due to their efficiency and built-in timestamp feature.
- Rule 3: Leverage rich documents, focusing on how they allow for a more holistic representation of data compared to relational databases.
- Rule 4: Use array keys effectively to optimize queries and document structure.
- Rule 5: Implement atomic operators to enhance efficiency during updates and modifications.
- Rule 6: Pay attention to indexing to ensure queries run efficiently without causing significant delays.
- Examples: Banker discusses different applications of MongoDB, including use cases for SourceForge and GitHub, showcasing how they effectively utilize MongoDB's strengths. He also highlights the differences between MongoDB and CouchDB, emphasizing MongoDB's advantages in ad-hoc querying and atomic updates.
- Additional Takeaways: The talk concludes with recommendations regarding performance optimizations, the importance of keeping indexes in RAM, and considerations for using GridFS for managing binary data. Banker addresses various questions from the audience at the end, further clarifying MongoDB's capabilities and flexibility.
Overall, the presentation provides attendees with a thorough understanding of MongoDB's features and practical guidelines to maximize its utility in software development projects.
00:00:14.920
All right, we'll go ahead and get started. The name of my talk is 'MongoDB Rules', which has kind of a nice double ring to the title.
00:00:21.560
MongoDB is a database; it's an open source database written in C++ and document-based. I'll talk a lot about it.
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Real quick, how many people have heard of MongoDB? Can you just raise your hands? Okay, cool.
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How many people have used MongoDB? All right. I hope to speak to the middle during this talk.
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It's not going to be entirely introductory. I want to give a set of rules for using it well.
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Hopefully, in the process, if you haven't heard about MongoDB, you'll get a sense of what it's like.
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Before I get into the meat of my talk, there's some really important business I want to take care of.
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Okay, the important business: my name is Kyle Banker, and I work for a company called Tenen.
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Tenen is the company that sponsors MongoDB's development. You can find me at kyoten on Twitter.
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We're at mongodbe.org, where you can find very good documentation and download binaries.
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This means you don't have to compile it, and you can get it running pretty quickly.
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If you ever have a question about MongoDB, the Google Group is a great place to find answers.
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Typically, you can get an answer in about 20 seconds, so check that out.
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Now, I want to ask one more question: how many people here are using some type of NoSQL database?
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Okay, so maybe like 30 to 40 percent of you.
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Of those using a NoSQL database, raise your hand if you are using it for some kind of operational reason.
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In other words, you need extra performance or scalability. So maybe half of you.
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When the discussion comes to NoSQL databases, most of us are concerned about performance and scalability.
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I think MongoDB addresses those concerns.
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However, there's another side to it. You might not have a scaling problem, but there may still be a reason to use a NoSQL database like MongoDB.
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It makes things easier to understand, and that was my initial reason for being attracted to it.
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You'll see why that might be if you haven’t tried it already.
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Rule number one is to get to know the real MongoDB.
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What does that mean? There are some pretty good object mappers out there.
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They give you a very Active Record-like interface, but they do hide some of the ease of actually working with the database.
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When you're working with a relational database, your object-relational mapper is absolutely necessary.
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In the case of MongoDB, it's not that difficult because we deal with something called a document.
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A document is something in JSON, so when I say get to know the real DB, I mean to start playing with it.
00:03:03.680
We have a shell, and our shell isn't SQL; it's JavaScript.
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I was able to build this little 'Try MongoDB' site in the spirit of 'Try Ruby'.
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You can go to tr.mongod.com and actually go through a little tutorial and play with a real MongoDB instance in a JavaScript shell.
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This gives you a sense of how the thing works.
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When you're done with that, you can download the actual shell and do a lot of the same things.
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The other thing about the real MongoDB is using the Ruby driver.
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It's a very natural way of working with your data.
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We build a connection in the same way you choose a database with a relational database.
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Here's an example: we're requiring Ruby gems and requiring the driver. We build a connection.
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We choose a database and choose a collection, in this case, we're working with a 'page views' collection.
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Here's a sample document, and it's just a hash, right? It's a Ruby hash pointing to a couple of different types.
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We got a date type in there. We have some strings, an integer, and we just pass that to our collection object and click save.
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Now what happens when you click save? What happens when you call save on an object like that?
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The driver is responsible for creating the primary key. Everyone sees that '_id' field—it's an ObjectID.
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I'll explain what that consists of. You can use anything as an _id, but the MongoDB ObjectID works kind of like this.
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The first four bytes are a timestamp, the next three bytes are machine ID, the following two are process ID, and the last are a counter.
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It's a 12-byte ID. One of the nice things about using MongoDB's ObjectIDs is that they include a timestamp.
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If you do a query and sort by object ID, you basically get things sorted by created at.
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You can also extract the creation time from the object ID, so there's value in actually using that.
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Additionally, the driver serializes the Ruby hash into a binary dictionary form called BSON.
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You can read all about it at bsonspec.org; it's not difficult to understand.
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All drivers, regardless of which one you use, serialize to BSON.
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So when we call save, we add an _id, create that _id if you haven't done so yourself, serialize to BSON, and send it along the socket.
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Since we already have the ID, we don't have to wait for any response from the database.
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The philosophy is that you can always add machines and add clients.
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If you're going to pound the database, you shouldn't have to wait for a response.
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So the drivers do a little more work. Rule number two is to use ObjectIDs.
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Many people in the Ruby community decide to use a string version instead.
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ObjectID is a proper BSON type. It's a bit more efficient because it's 12 bytes versus 16.
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It has that timestamp built into it, and it's the standard.
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If you're working with ObjectIDs, that's just one gotcha to keep in mind.
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The next thing is to use rich documents, which is one of the really important aspects.
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MongoDB allows you to represent rich data structures within a single document.
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I have a document here representing a cart or an order.
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In the interest of talking about the advantages of rich documents, I want to show an anti-pattern.
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This database diagram is a relational database for Magento, which is similar for other large e-commerce applications.
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You notice many small tables. If you look at the different entities, you'll see lots of tables.
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What are these tables doing? A lot of you know exactly what they’re doing.
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You're simulating a flexible schema with these tables.
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Each of these tables has a different field representing a different type.
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If I need to add a dynamic integer attribute to a product, I use that specific table.
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If I want to add a dynamic string attribute, I need another specific table.
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The important question becomes: what is the join?
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You can't just open a relational database console and see what a product looks like.
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You can't reason about this style of data.
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I think it's easier to reason about document-style data.
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If I can represent all those relations in a document and modify that document as needed, it's a win.
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I can easily question, for example, a product in MongoDB and get a complete representation.
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MongoDB is human-oriented.
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We can conceive of the data with a document model and look at objects as holistic entities.
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This is a huge advantage of a document-oriented database, and a good reason to use it.
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Going back to our order, we see a couple of line items.
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For example, I have a 'lumberjack laptop case' I bought online.
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Initially, I thought it looked kind of cool, but upon receiving it, I realized it looks like a lumberjack's laptop case.
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That’s my lumberjack laptop case.
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You can see I track the SKU and the list price in the same document.
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I can also store the shipping address in the same document and different types of totals at the bottom.
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The line items point to an array of other objects.
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This is represented economically in this object.
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There’s value when you can query or manipulate these items easily.
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MongoDB has dynamic queries, similar to a relational database.
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I don’t have to define everything upfront; I can perform simple queries.
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For example, I’m looking for something by user ID.
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When I create an index, notice that it's on an inner object.
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I’m creating it on line items SKU, so you see in my line items object, it’s line items.SKU.
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When I do that query, I can utilize an index.
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The last thing I want to illustrate: the most expensive product purchased in the last week.
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I'm creating a date range and passing those as a document.
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My query is a document, and so is my data.
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I'm saying where createdAt is greater than last week, less than today, sorted by total, and limited to one.
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It can use an index, making it an extremely efficient query.
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These are some other query operators. We can pass in arrays.
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There are many special query operators that allow you to do much like a relational database.
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Rule number four is that array keys rule.
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Let me give you an example to simplify tiny relationships.
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Tags are a good example: I'm having a social news site.
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A link has a title, a URL, and an array of tags.
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For example, 'Tech', 'startups', and 'time waster'.
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Now I can create an index on the array field.
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When I do this query, it's efficient, and I'm not wasting any time.
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The query is straightforward without special iterating over documents.
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In a relational database, I might have a table with userID, tagID, and a piece of text.
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In contrast, we can eliminate that level of normalization and put it in the document.
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The second aspect of the key rule is representing complex things like many-to-many relationships.
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I can represent products and categories, for example.
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Here, the category is just a title, and the product is a record.
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The category IDs can be an array of ObjectIDs.
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You can find all products in a category with a simple query.
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You can perform the same process in the reverse; no join table required.
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Rule number five is to use atomic operators.
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Many of you know that Redis is famous for atomic operators; MongoDB has them too.
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They allow you to modify a single key in place, which can be efficient.
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For example, if you're building a site like Hacker News, implement upvote functionality.
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Here's how the query looks; we find a post with a given ID.
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The update includes an atomic operation. We add the voter ID while incrementing votes.
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We don't have to pull down the document and modify it; all can be executed in one operation.
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An example includes concert seats; we use the command 'find and modify', a special command.
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We look at the seats collection and search for a particular concert seat without an expiry date.
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The atomic update operation sets an expiry date in the next 15 minutes.
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If the update succeeds, I get the document back, ensuring consistency.
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While we don't have full transactions, atomic modifiers are versatile.
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Some update operators include incrementing values, setting values, or interacting with arrays.
00:13:24.000
Let's talk briefly about map-reduce.
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People have heard of map-reduce; think of it as functional programming for aggregation.
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It’s particularly useful for large computations.
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I’ll provide an example: finding the total of orders from a given zip code.
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We emit the zip code as our key in the map function.
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Values convert to totals, and our reduce function sums them.
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Map-reduce is generally for ETL rather than live applications.
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Rule number six is indexes. In MongoDB, a collection supports up to 40 indexes.
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Indexes behave similarly to those in relational databases.
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Knowledge about relational databases is useful for building indexes in MongoDB.
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I've witnessed instances where large databases freeze when creating indexes, so be smart about it.
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It's crucial to ensure that your queries match your indexes.
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Be cautious about defining keys and indexing them.
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GFS is a specification for storing binary data in MongoDB.
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You can store large files in MongoDB, and it works on the driver level.
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For example, if you have a picture of a lumberjack, you can use GFS to store it.
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You define a grid object, open a file, and perform operations using GFS's API.
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Everything in MongoDB works on the collection level.
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We have a files collection for metadata and a chunks collection for the data.
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This data organizes efficiently and can be fast.
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A bit about MongoDB's speed and durability trade-off.
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One technique used is memory-mapped files, which maps files onto virtual memory.
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Writing information to disk feels like writing to memory.
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The kernel manages what data resides in memory versus on disk.
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MongoDB enforces an fsync to disk every minute.
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If you shut down the master node, some corruption could occur.
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We suggest replicating, using a master-slave setup, to improve durability.
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Adjust the fsync if you want to fsync every 30 seconds.
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MongoDB believes speed using memory-mapped files is more critical.
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Multiple nodes better achieve durability.
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Here are some performance notes: the drivers do extensive work.
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Drivers generate ObjectIDs and serialize to BSON.
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The Ruby driver can be slower due to the nature of Ruby.
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When benchmarking Ruby, don’t benchmark on a single node.
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Make sure to benchmark across multiple processes.
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If you're pumping data into MongoDB via Ruby, you may not see results.
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Using other languages, like Closure on the JVM, shows better core performance.
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Additionally, embedded documents provide both a mental and computational win.
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We avoid joins and can store related data in a single embedded document.
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Queries should always use indexes.
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Keep your working set and indexes in RAM, just like with a relational database.
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We've seen issues when databases won't scale due to insufficient memory.
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Auto-sharding becomes necessary if you can't keep everything in RAM.
00:18:06.960
Data is chunked and routed through an S client, distributing updates.
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All shards are autonomous and don't communicate, allowing for scalability.
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Not every setup requires sharding, but it's effective.
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Two production cases: SourceForge uses MongoDB for project pages.
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They store all project information in a single document for efficiency.
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Currently, GitHub utilizes MongoDB for backend analytics.
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Another example: the Harmony app simplifies its schema by switching from MySQL.
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There's a diversity of other production deployments, and I welcome questions.
00:18:59.920
Are you familiar with Rescue? Do you think you could build that in MongoDB?
00:19:06.640
Now that you have the 'find and modify' command, you can easily set the status.
00:19:13.920
I think someone is working on it.
00:19:21.360
Can you describe what an embedded document is and talk more about it?
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Embedding means having a document containing other documents.
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In a simple blog, for example, you could store all comments inside the post object.
00:19:41.920
You can represent any structure with a Ruby hash in MongoDB.
00:19:48.760
There are many differences between CouchDB and MongoDB.
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CouchDB uses HTTP; MongoDB uses a binary protocol over a socket.
00:20:01.760
CouchDB has a multi-master replication scheme; MongoDB scales via auto-sharding.
00:20:07.760
In CouchDB, you must build indexes using map-reduce; MongoDB allows more ad-hoc querying.
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Additionally, MongoDB supports atomic updates, while CouchDB does not.
00:20:20.560
When starting with Ruby, many wrote like Java in Ruby.
00:20:26.960
What are some high-level designs to break our SQL background?
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I have documents on data modeling available on our website.
00:20:39.440
Trivial relationships should be contained in a single document.
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In more complicated relationships, consider your use cases.
00:20:53.680
There's nothing wrong with normalizing in MongoDB, it is designed for that.
00:20:59.760
People worry about needing short key names; in practice, it hasn't been an issue.
00:21:06.640
Character set encoding: everything needs to be in UTF-8.
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Currently, we don’t support various other character sets.
00:21:18.000
There are many binary serialization standards like BSON versus others.
00:21:24.240
BSON is preferred since it maps nicely to known data structures.
00:21:31.320
What is the potential for accessing GridFS directly from web servers?
00:21:36.840
People have built tools for that; it’s completely possible.
00:21:43.920
One of my coworkers is developing an Nginx module to interact with GridFS.
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The roadmap includes 2D Geo-indexing and sharding.
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It’s critical for us to support 100% automatic failover for sharding.
00:22:02.960
Currently, managing it manually adds complexity.
00:22:09.680
There’s the concept of a replica set; I can tell you more about it later.
00:22:15.680
If you go back to the previous diagram, shards could appear as single points of failure.
00:22:22.640
The S client is lightweight and lives alongside application servers.
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Your application could have multiple S clients, which reduces failure risk.
00:22:36.400
Explaining the query process, we have an 'explain' command to track query paths.
00:22:42.720
If needed, I can provide further clarification on performance optimization.
00:22:49.680
Thank you very much for your time.
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