Redis Cookbook: Practical Techniques for Fast Data Manipulation
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This book spans pages so it is a quick read. But the information is dense and the case studies are phenomenal. The author builds on top of fundamental Redis knowledge teaching you how to create a notifications system, how to use HyperLogLog, and how to check analytics data on the backend. There is no single best way to use Redis because each scenario has its own goals. But this book introduces a number of best practices that you can apply to any project to improve your workflow and improve your knowledge of Redis in the process.
Before following this book you should have experience building custom applications from scratch and you should already understand a bit about database engines. However you can pick up this book with little prior knowledge of Redis and still work through the content if you have the tenacity to push through. With the skyrocketing popularity of Node. This book ultimately teaches you how to build scalable Node. The author assumes very little prior knowledge of Node or Redis, and both get covered in great detail. Each chapter covers a variety of new features and the author frequently builds upon 3rd party frameworks like Express.
Some chapters even get into Grunt. Redis is optional for Node but your stack will be greatly improved with Redis attached.
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This book is not a Redis-specific book. However the concepts and methodologies do pertain to a Redis stack where the goal is handling large databases with speed and efficiency. Designing Data-Intensive Applications teaches web engineers and architects how to build applications that easily scale. In this book he covers the concepts of scalability and reliability when architecting a new project.
Redis Cookbook [Practical Techniques For Fast Data Manipulation]
Martin covers long term goals with caching and key-value stores like Redis. He also shares the most popular dev frameworks that can stand the test of time. This book is very long with pages full of advice and best practices for scalable projects. Sometimes quick intro guides are the best resources to help you solve minor problems in your stack. Instant Redis Optimization How-to by Arun Chinnachamy spans 56 pages of handy tips for optimizing your Redis workflow. Unfortunately there are not many Redis-only optimization guides to choose from.
Getting beyond a simple installation and caching setup requires new challenges and lots of practice. He talks about big data and shares topics for distributed data with Redis. He also shares tips and code snippets using C and Lua for server-side development. Each chapter has practical tips along with screenshots to guide you through the process.
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Overall this book is quite detailed yet short. This cookbook is a tad different than others because it offers a deeper look into common Redis features along with customized recipes. The Redis Cookbook is really small with only 75 pages in total. You might wonder why this is even worth the effort.
A lot of recipes simplify more advanced data structures and DB organization styles. The author covers many common pitfalls offering solutions on how to avoid issues during your development cycle. First here:. That redis client should just look like it's hanging. It is. Ok, now in a different shell, and another redis-cli, do this:. Now if you look back at the first redis-cli, you'll see that it immediately popped the value off the list and said something like:. That rules! You get to make up names of channels on the fly just like variable names.
Again, the channel does not have to exist or be predefined in any way. By naming it, you cause it to exist. That means you can subscribe to a bunch of channels at once. This is useful for, say, log routing. Ok, enough of the jibba jabba. Here's an example, again using two separate redis CLIs, to show how it works. In one redis-cli, do this:. In the same way the BLPOPping redis-cli blocked, this redis-cli is not capable of doing anything but listen for messages.
Now in a second redis-cli, do this:. In that case, just check the name of the channel on the received message. It's a common pattern to use numerical timestamps for ranking members in a ZSET. I'll show this in the node console, to illustrate how the optional parameters are done. Whenever someone logs into my site, I record them in my 'last-login' zset. I'll simulate some logins like so:. Since this is a set, lloyd only appears once, with updated login timestamp.
ZRANGE gives you everything in the zset, in order of score, from the beginning offset to the end offset. As you can see, the scores are sorted smallest to biggest. As a rule, the name of the optional argument is given as a string in the node function call. In addition to getting elements by their rank in the ZSET, you can get them by score. For example, to see everyone who's logged in in the last hour, you could do:. TTL tells you the time-to-live for the key, if there is an expiration set on it.
Otherwise, you get Note that you get -1 both when there's no expiration and when there's no key. I have used this recipe in production. Bit strings are just that: compact representations of ones and zeroes. I haven't yet used bitstrings in production myself. The score for a zset value is a float.
You can be clever and sneak extra data in by encoding something else after the decimal. This adds processing time and complexity for your application, but could save you space in redis for large data sets. My most favorite flavor in redis is the ZSET. ZSETs are a large part of the answer to most of life's problems. And more than that, they are just fucking awesome. Fast, efficient, versatile, simple, powerful.
Talk to your doctor today to find out whether ZSETs might be right for you. You may encounter articles on the web trying to scare you away from using lots of small zsets due to their memory consumption. If they were written before Redis 2. Storing huge globs of JSON. How can I hate this? It's so tempting to do so!
And it's so easy! It's just a stringify and a SET away. In some cases, stashing JSON or passing it over queues and pubsub channels could be the right thing to do. I think logging data is a good candidate for this.
But before you start cramming huge messes of stringified JSON in your db, check in with your gut and ask yourself:. If the structure matters, consider using redis hashes or flattening your data across your keyspace. Maybe the data doesn't belong in your redis memory at all? For example, if you have a job queueing system, you might want redis to manage the queues themselves, and store pointers for the job data which might be retrieved by workers from some different, disk-resident db.
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Memory usage in redis is important to think about. The speed benefits of redis are lost when you overflow into disk memory. As a conservative calculation, I multiply all the bytes I think I might store and multiply by 10, which antirez once recommended as a worst-case factor for data structure overhead. So for a test user account creation and verification service called "Persona Test User," I have these keys:.
The ptu: prefix makes it extra clear what these keys are for. Even if I have a private redis db for this app, this is descriptively useful and adds a measure of safety. In the case where you want to do something akin to a join, like, say, associate an email and a remote url to store a BrowserID assertion, just make a new key. In this case, it might be:. Get the latest and greatest from MDN delivered straight to your inbox. Please check your inbox or your spam filter for an email from us.
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Languages No translations exist for this article. Add a translation. There are three reasons to look at the docs for every command you use. Time complexity - Each command's complexity is given in Big-O notation. This is immensely helpful.
Related commands - An ever-growing list of related commands is given on the right-hand side of the page. Recipes - At the bottom of many pages is a section that gives usage patterns or recipes for the command under discussion. There are many insightful ideas to be found here. Getting Started with Redis Before you go any further in this document, make sure you have played with redis for at least 5 or 10 minutes.