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Mastering Redis

By Vidyasagar N V , Jeremy Nelson
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  1. Free Chapter
    Why Redis?
About this book
Redis is the most popular, open-source, key value data structure server that provides a wide range of capabilities on which multiple platforms can be be built. Its fast and flexible data structures give your existing applications an edge in the development environment. This book is a practical guide which aims to help you deep dive into the world of Redis data structure to exploit its excellent features. We start our journey by understanding the need of Redis in brief, followed by an explanation of Advanced key management. Next, you will learn about design patterns, best practices for using Redis in DevOps environment and Docker containerization paradigm in detail. After this, you will understand the concept of scaling with Redis cluster and Redis Sentinel , followed by a through explanation of incorporating Redis with NoSQL technologies such as Elasticsearch and MongoDB. At the end of this section, you will be able to develop competent applications using these technologies. You will then explore the message queuing and task management features of Redis and will be able to implement them in your applications. Finally, you will learn how Redis can be used to build real-time data analytic dashboards, for different disparate data streams.
Publication date:
May 2016
Publisher
Packt
Pages
366
ISBN
9781783988181

 

Chapter 1. Why Redis?

Why Redis? Or, why any technology? Such questions are often mumbled under the breath or asked by the more brave, cynical, or knowledgeable when encountering any new technology or service. Sometimes, the answer is obvious, the technology or service offers features and functionalities that meet an immediate need or solves a vexing problem. In most situations, the reasons for adopting a technology may not be as clear-cut or as apparent or are cloaked in sometimes hyperbolic or indecipherable marketing jargon. Depending on your needs, Redis falls somewhere closer to the obvious end of the spectrum instead of a marketing sales pitch. You may already know and have used Redis for some uses, such as meeting a data storage need or service requirement for an application, but you may not be aware of all that Redis can do or how other people are using Redis in their own organizations. Redis, best known for its speed, is not only fast in its execution but also fast in the sense that solutions built with Redis have fast iterations because of the ease in configuring, setting up, running, and using Redis.

The growing popularity of Redis, an open source key-value NoSQL technology, is a result of Redis's stability, power, and flexibility in executing a wide range of data operations and tasks in the enterprise, REmote DIctionary Server (Redis), is used by a diverse set of companies from start-ups to the largest technology companies such as Twitter and Uber, as well as by individuals and teams in government, schools, and organizations. We'll start this chapter with a short survey of a few popular design patterns for Redis and then, provide practical advice on determining whether Redis is the right choice for you.

We'll then go through a detailed example of how Redis a legacy metadata format used by public and academic libraries – including some museums – to illustrate Redis's flexibility and power with just three data structures and an intentional key design. Finishing this chapter off, we'll touch upon recently added functionalities and commands to Redis.

 

Is Redis right for me?


A relatively common question posted to the general Redis e-mail mailing list, asks whether Redis is a good choice for a variety of uses, such as running reviews on a website, caching results from MySQL databases queries, or meeting other specific requirements that the poster might have for his/her project, product, website, or system. In general, Redis excels as a tool for a fast read/write of data and has been used with great success by small and large organizations alike for a wide range of uses. Salivator Sanfilippo makes a strong case that Redis does not need to replace the existing databases but is an excellent addition to an enterprise for new functionalities or to solve sometimes intractable problems1.

Being a single-threaded application with a small memory footprint, Redis achieves durability and scalability through running multiple instances on the current multicore processors available in data centers and cloud providers. With Redis-rich master-slave replication and now with Redis clusters are released in production, creating multiple Redis instances are relatively cheap operation in terms of memory and CPU requirements, allowing you to both scale and increase the durability of your larger applications.

Redis allows you to conceptualize and approach challenging data analysis and data manipulation problems in a very different manner as compared to a typical relational data model. In an SQL-based relational database, the developer or database administration creates a database schema that organizes the solution domain through normalizing the data into columns, rows, and tables with connecting joins through foreign-key relationships.

Even other NoSQL data storage technologies such as MongoDB or Elasticsearch require the data to be modeled as JSON document data structures first before being loaded into the actual storage. Redis skips this intermediate but necessary step in these other technologies, by just providing sets of commands for specific data structures such as strings, lists, hashes, sets, and sorted sets. In this approach, you are algorithmically interacting with your data, constructing solutions directly with how the data is stored in Redis and the available commands, and enabling a more direct tuning and monitoring of the underlying operating system's memory and hard disk space.

Thinking how data is represented and managed as basic computing data structures such as lists, hashes, and sets, allows you to grasp both positive and negative characteristics of the data and its structures in a more fundamental, mathematical fashion. Going through the intermediate structuring process such as normalizing your data for a relational database or converting it into a JSON document for MongoDB or Elasticsearch, while valuable, imposes a structure that Redis does not. As you architect your solutions, you may discover that your data and your problem need more of the persistence and structure of a technology other than Redis, but in the meantime, your exploration of the properties and the structure of data in Redis will be a useful exercise because of this algorithmic approach to your information and problem.

Redis may not be the best technology to use when you have a large amount of infrequently used data that does not require immediate access. An SQL-based relational database or a document-store NoSQL technology such as CouchDB or MongoDB may be a better choice than Redis. However, with Redis Cluster now fully supported as of version 3, large datasets can be sharded and used in Redis as a distributed key-value data store. As more organizations and individuals gain experience with the use of Redis Cluster, expect that this reason to not choose Redis for a project will fade away.

 

Experimenting with Redis


Redis's rich set of data types allows for easy and fast experimentation of data-based algorithms and approaches on information. In my own experience with Redis, this ability to quickly model and use solutions is based on the characteristics of the different data structures of Redis and the flexibility in defining the structure and syntax of the keys. I was impressed and excited to be able to name a chunk of malleable data and to relate this name with other keys through the naming semantics of the key. This is a great feature of Redis that is sometimes underappreciated as to how powerful and useful a tool it can be in developing and understanding your data.

I first started experimenting with Redis in 2011 as a metadata and systems librarian at Colorado College at the base of the Pikes Peak Mountain in Colorado. Most libraries around the world store and structure their bibliographic data in a somewhat surprisingly durable binary format called, MAachine-Readable Cataloging (MARC), substantially developed in the late 1960s by Henriette Avram of the United States Library of Congress. The current version, MARC 21, is officially supported by the Library of Congress (however, it is in the process of replacing MARC with a new RDF-based linked data vocabulary called BIBFRAME). MARC21 initially encoded information about the books on the library's shelves and has been extended to support e-books available for checkout; video, music, and audio formats; physical formats such as CDs, Blu-ray discs, and online streaming formats; and academic libraries. In fact, an increasingly large percentage of its budget is devoted to the purchase of journal articles through online publishers and electronic-content vendors.

The MARC format is made up of both fixed length and variable-length fields numbered in the three-digit range of 001–999, which in turn can have either character data or subfields with data. In addition, each field can have up to two indicators that modify the meaning of the field. Two of the most common and important MARC fields are the 100 Main Entry – Personal Name field and the 245 Title Statement field. Here is an example from David Foster Wallace's book Infinite Jest:

=100  1\$aWallace, David Foster
=245  10$aInfinite jest :$ba novel$cDavid Foster Wallace

To use this MARC data in Redis, each MARC record was a hash key modeled as marc:{counter} with the counter being a global incremental counter. Each MARC field is a hash with the key modeled as marc:{counter}:{field}. As some MARC fields are repeatable with different information, the hash key would include a global counter such as marc:{counter}:{field}:{field-counter}. Simply storing these two fields would result in the following six Redis commands:

127.0.0.1> INCR marc
(integer 1)
127.0.0.1:6379> INCR marc:1:100 
(integer 1)
127.0.0.1> HSET marc:1:100:1 a "Wallace, David Foster"
OK
127.0.0.1:6379> INCR marc:1:245 
(integer) 1
127.0.0.1:6379> HMSET marc:1:245:1 a "Infinite jest :" b "a novel" 
c "David Foster Wallace"
OK
127.0.0.1:6379> HGETALL marc:1:245:1
1) "a"
2) "Infinite jest :"
3) "b"
4) "a novel"
5) "c"
6) "David Foster Wallace"

This key structure in Redis looks like the following:

MARC in Redis

The storage of MARC data in Redis can be accomplished with just a single Redis data type, a hash, along with a consistent key syntax structure. To improve the usability of this bibliographic data in Redis and to realize a very common use case of retrieving library data as a list of records sorted alphanumerically by title and author name (in library parlance two access points) is also accomplishable with other Redis data types such as lists or sorted sets.

Representing MARC fields and subfields in Redis by using hashes and lists was informative. Further, I wanted to see if Redis could handle other types of book and material metadata models that were being put forward as replacements for MARC. The Functionality Requirements for Bibliographic Record, or FRBR, was a document that put forward an alternative to MARC and was based on entity-relationship (ER) models. The FRBR ER model contained groups of properties that were categorized according to abstraction. The most abstract is the Work class, which represents the most general properties to uniquely identify a creative artifact with such information as titles, authors, and subjects.

The Expression class is made of properties such as edition and translations with a defined relationship to the parent Work. Manifestations and Items are the final two FRBR classes, capturing more specific data where Item is a physical object that is a specific instance of a more general Manifestation.

With few actual systems or technologies that implement an FRBR model for library data, Redis offers a way to test such a model with actual data. Using existing mappings of MARC data to FRBR's Work, Expression, Manifestation, and Item, the MARC 100 and 245 fields from the above would be mapped to an FRBR Work in Redis as shown by these examples of using the Redis command-line tool, redis-cli, to connect to a Redis instance:

127.0.0.1:6379> HMSET frbr:work:1 title "Infinite Jest" "created by" "David Foster Wallace"
OK

This new work, frbr:work:1 can be associated with the remaining classes with the following Redis keys and hashes:

127.0.0.1:6379> HMSET frbr:expression:1 date 1996 "realization of" frbr:work:1
OK
127.0.0.1:6379> HMSET frbr:manifestation:1 publisher "Little, Brown and Company" "physical embodiment of" frbr:expression:1
OK
127.0.0.1:6379> HMSET frbr:item:1 'exemplar of' frbr:manifestation:1 identifier 33027005910579
OK

In the previous example for Expression, a specific date is captured along with a relationship back to frbr:work:1 through the realization of a property. Similarly, the frbr:manifestation:1 hash has two fields; a publisher, and the physical embodiment of. The physical embodiment of field's value is the frbr:expression:1 key that links the Manifestation back to the Expression. Finally the frbr:item:1 hash has a barcode identifier property and a relationship key back to the frbr:manifestation:1 hash.

In both the MARC and FRBR experiments, the Redis hash data structure provided the base representation for the entity. This strategy starts to fail when there can be more than one value for a specific property, such as when representing multiple authors of a work. The first attempt to solve this problem for those properties with multiple values is by creating a counter for each MARC field as outlined above. For example, the MARC 856 field – Electronic Location and Access – stores the URL for e-books or other material that has a network-resolvable URL. If we want to add two URLs to the preceding MARC example, such as a link to the book in Google Books and a wiki on the book, the Redis commands would be as follows:

127.0.0.1:6379> INCR global:marc:1:856
(integer) 1
127.0.0.1:6379> HMSET marc:1:856:1 ind1 4 ind2 1 u https://books.google.com/books?id=Nhe2yvx6hP8C
OK
127.0.0.1:6379> HMSET marc:1:856:2 ind1 4 ind2 2 u http://infinitejest.wallacewiki.com/
OK

This naming approach for the MARC keys meets the requirement for repeating MARC fields, but how can we support the edge case wherein a single MARC field has multiple, repeating subfields? The first pass to solve this problem may be to store a string with some delimiter between each subfield as the value for a particular filed in the MARC. This would require additional parsing on the client side to extract all the different subfields, and we would lose any additional advantages that Redis may provide if these multiple subfields were stored directly in Redis. The second approach to solving the MARC field with multiple subfields in a MARC field would be to further expand the Redis key syntax and use a list or some other data structure as value for each subfield key. Expanding the MARC 856 example, if we wanted to add a second e-book URL, maybe a URL to the Amazon Kindle version, it would look like the following in Redis:

127.0.0.1:6379> LPUSH marc:1:856:1:u https://books.google.com/books?id=Nhe2yvx6hP8C http://www.amazon.com/Infinite-Jest-David-Foster-Wallace/
(integer) 2
127.0.0.1:6379> HSET marc:1:856:1 u marc:1:856:1:u
(integer) 0

Storing multiple subfields in a Redis list works well, but what if I don't want any duplicate values in a MARC field's subfields? This can be easily solved by the use of Redis's set data type, which, by definition, only contains unique values. The use of sets for the subfield values seems like a good solution, but it fails, if we need to keep the ordering of the values in the subfield.

Fortunately, Redis's sorted set data type fits our use case admirably by ensuring a collection of unique subfield values with no duplications, and finally maintaining, the subfield ordering. The resulting Redis commands for storing the URLs of a book in the MARC 856 field would look the following:

127.0.0.1:6379> DEL marc:1:856:1:u
(integer) 1
127.0.0.1:6379> ZADD marc:1:856:1:u 1
https://books.google.com/books?id=Nhe2yvx6hP8C 2
http://www.amazon.com/Infinite-Jest-David-Foster-Wallace/
(integer) 2
127.0.0.1:6379> ZRANGE marc:1:856:1:u 0 -1 WITHSCORES
1) "https://books.google.com/books?id=Nhe2yvx6hP8C"
2) "1"
3) "http://www.amazon.com/Infinite-Jest-David-Foster-Wallace/"
4) "2"

In this example, we examined how to represent a legacy format for library data called MARC, and how MARC's fields and subfields data can be stored in Redis by using hashes, and how the storing of subfields changes as more requirements are met, moving from storing subfields first as Redis lists, followed by sets, and finally finishing by using the sorted set data type. This iterative experimentation hopefully illustrates an important reason for using Redis, namely the ability to quickly test out different methods of storing data and how the characteristics of different Redis data types such as hashes, lists, sets, and sorted sets can be used to represent both the data and some of the requirements for storing and accessing this data.

 

Popular usage patterns


A very popular use pattern for Redis is as an in-memory cache for web applications. Redis is available as a caching option for popular web frameworks such as Django, Ruby-on-Rails, Node.js, and Flask. As a popular caching technology Redis excels in web applications for storing new data while evicting stale data. For web applications, the cached data can range from single HTML character strings, widgets, and elements to entire web pages and websites.

By utilizing Redis's ability to set an expiration time on a key, one of Redis' popular caching strategies called Less Recently Used (LRU) is robust enough to handle even the largest web properties, with the most popular content remaining in cache but stale and little-used data being evicted from the data store. This caching use case doesn't assume that the original web element or page is generated from the data in Redis; most likely, the web content was dynamically generated from other sources of data with Redis, in this use pattern, and operates as an excellent web caching layer in this setup.

The second popular use pattern for Redis is for the metric storage of such quantitative data such as web page usage and user behavior on gamer leaderboards. Using bit operations on strings, Redis very efficiently stores binary information on a particular characteristic. Usage for a website could be stored with a key constructed from a date such as page-usage:2016-11-01, which has a string attached with a bit flipped to 1 the first time a web page is accessed by a user.

The daily usage for the website for November 1 can be obtained through a simple BITCOUNT Redis command on the page-usage:2016-11-01 key. In a 2011 blog post, individuals at a start-up named Spool explain in detail how they use bitmaps and Redis bit operations to store the user activity on their website with this design pattern.

The third popular Redis use pattern is as communication layer between different systems through a publish/subscribe (pub/sub for short) model, where one can post messages to one or more channels that can be acted upon by other systems that have subscribed to or are listening to that channel for incoming messages.

Typically, publishers do not need to know the specific subscribers to send messages to them (say in a point-to-point messaging model); only the message contents and what channel to send the message should be known. Similarly, a subscriber does not need to know individual publishers, only the channel to receive messages. The pub/sub pattern is nice because it scales easily, and the publishers and subscribers can be very different programs and systems.

 

Redis isn't right because …try again soon!


As an active open-source project, Redis adds new functionality and improvements that may solve a problem that you or someone in your organization decided it wasn't suited for in the past. Optimizing the use of such a valuable and functional tool as Redis means understanding its recent history and keeping current with new functionality being developed and tested for inclusion in the latest stable version of Redis. Redis follows a common semantic versioning pattern of major.minor.patchlevel with a minor even number denoting a stable version and an odd minor number an unstable branch.

For example, the Redis 2.8.9 release introduced two of the more significant improvements, namely the HyperLogLog, a highly efficient data structure for a population estimate and of unique elements, and the new ZRANGEBYLEX, ZLEXCOUNT, and ZREMRANGEBYLEX commands for sorted sets. Both these are improvements that will be discussed at length in Chapter 2, Advanced Key Management and Data Structures. Redis Cluster – released for production use in early 2015 with Redis version 3.0 – is one of most important additions to the Redis ecosystem, which we will go over in much more detail in Chapter 6, Scaling with Redis Cluster and Sentinel.

For the next major release Redis added Geographic Information Systems (GIS) commands and modified sorted sets along with new Lua scripting support for Redis Cluster and a new Lua debugger in Redis version 3.2. To visualize the rate of change to the Redis code base, the following graphic shows the rate of change in the Redis code base during the Redis 2.x series to Redis version 3.0.

Be aware of the dynamic nature of Redis development when asking yourself, why Redis? The limitations that you thought Redis had might no longer be the case and as you continue to grow your knowledge and improve your skills in mastering Redis, keeping up with Redis changes should a critical priority as you improve your existing technology and build new and exciting opportunities for the future.

 

Summary


The decision as to whether Redis is the correct choice for a new project or to solve a data problem you might be experiencing really depends on the nature of your data and what you're trying to accomplish with your project. Redis, unlike relational databases or NoSQL document stores, does not require you to structure your data first before using it. Redis provides a direct, more algorithmic manipulation of your data through the use of a variety of data structures such as lists, hashes, sets, and sorted sets. Even if Redis is not your final choice, the exercise of breaking down your data into these data structures will help deepen the context and the analysis of the issue that you're trying to solve. A detailed example of such experimentation was given while representing a legacy library standard called MARC in the basic Redis hashes, lists, sets, and sorted sets. We then briefly reviewed three popular design patterns for using Redis as a web cache, Redis as the backend for a gamer leaderboard, and Redis used as a publish/subscribe messaging system. We finish this chapter by illustrating some recent changes to Redis that expand the types of problems that Redis can be the primary data solution that in the past traditional SQL database or other NoSQL technologies may have been adopted instead.

In the next chapter, we are going to first examine Redis keys and the importance of organizing these keys with a Redis key schema generated either through a Redis object mapper or through manual documentation. Chapter 2 then introduces the Big O notation, followed by a systematic review of the basic Redis data structures and commands based on time complexity measures, Chapter 2 finishes with an introduction to some of the newer data structures and commands, including bitstrings and HyperLogLog.

About the Authors
  • Vidyasagar N V
  • Jeremy Nelson

    Jeremy Nelson is the metadata and systems librarian at Colorado College, a 4-year private liberal arts college in Colorado Springs. In addition to working 8 hours a week on the library's research helpdesk, providing information literacy instructions to undergraduates, and supervising the library's systems and cataloguing departments, Nelson is actively researching and developing various components and open source tools in the Catalog Pull Platform for use by Colorado College, the Colorado Alliance of Research Libraries Consortium, and the Library of Congress. He is also co-founder and CTO of KnowledgeLinks.io, a semantic web startup. His previous library experience includes jobs at Western State Colorado University and the University of Utah. Prior to becoming a librarian, he worked as programmer and project manager at various software companies and financial services institutions. His first book, Becoming a Lean Library, published in 2015, applies lean startup and lean manufacturing ideas to libraries and library operations. Nelson's undergraduate degree is from Knox College and his master's of science in library and information science is from the University of Illinois Urbana-Champaign.

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