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Okay, so hello everyone. Good morning.
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Thank you for coming to my session on the last day in the morning; I really appreciate it.
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Today, I'm going to talk about Datomic from Ruby on Rails. It's a straightforward title, and I'm excited to share my insights.
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My name is Yoko Harada, and you can find me on GitHub and Twitter under the username 'yokuharada'.
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I'm a JRuby core contributor and a JRuby enthusiast, which is quite important to remember.
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I'm also a contributor to the NoCalgary project, and I work for Pure Java at NoCalgary.
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As a Japanese person, I find some words a bit difficult to pronounce, including 'relevance'.
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Currently, I work for Relevance as a developer. Today, I will cover three main topics.
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First, I will introduce the Diametric gem. Then, I'll provide an overview of Datomic for those who are unfamiliar with it.
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Finally, I'll delve into the details of how the Diametric gem works.
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To start, the Diametric gem is an ActiveModel wrapper for Datomic.
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Using this gem, developers can interact with Datomic in a way that's familiar to Rubyists.
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For those who do not know, Datomic is a database that leverages immutable data.
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It's built on a different paradigm than traditional relational databases.
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The first thing I need to clarify is that the Diametric gem is hosted by Relevance.
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However, it is an open-source project, allowing anyone to contribute. I appreciate any pull requests from the community.
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Relevance sells Datomic licenses and provides consulting, but the gem itself is a third-party product.
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Now, I'd like to discuss something called coding time.
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Our employees are encouraged to work on projects of their choice during what we call '20% time,' which occurs every Friday.
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During last year's JRubyConf, I worked on a picture project as part of this initiative.
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Three developers have significantly contributed to the Diametric gem.
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Quentin introduced the project, followed by Ryan, and I joined later.
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At present, I am the only one actively contributing to the gem.
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The Diametric gem presents certain challenges. I will show examples of how Datomic functions later.
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When people mention Datomic, it often comes with the association of Clojure, which is the language primarily used with it.
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Many developers prefer to use Datomic with Clojure, but there is also a Java API available that can be accessed via REST services.
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For Rubyists, we will need to make some adjustments, which I will discuss further.
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Now, let's talk about Datomic itself.
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Datomic was created by Rich Hickey, the well-known creator of Clojure.
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There are often questions about whether Rich Hickey works for Relevance; the answer is no.
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Datomic is a joint venture between Rich and Relevance, and they collaborate on its development.
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Now, let's discuss the definitions related to Datomic and its categorization.
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It is classified as a NewSQL database, but naming conventions can be confusing.
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Datomic guarantees ACID properties akin to those found in traditional RDBMS.
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Rich Hickey designed Datomic with simplicity in mind, ensuring minimal schema and data structures.
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In Datomic, the schema is represented as simple arrays and hashes.
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Here's an example: don't worry too much about the schema itself.
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Clojure developers can define the schema, but the Diametric gem generates an appropriate schema for Ruby.
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A few key features distinguish Datomic from traditional RDBMS.
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First, in Datomic, data is treated as immutable.
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This architecture allows for reasoning over time effectively.
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For instance, if we have a table with a boyfriend's picture, when a new picture is added, the old one is not deleted; instead, a fact is created.
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This approach removes the need for data deletion, as Datomic implements this with a focus on immutability.
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Rich Hickey called this approach 'fact-oriented programming,' wherein facts are never altered.
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A good example of this is how GitHub IDs remain as facts that can be traversed over time.
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The ability to roll back to previous states is a significant advantage in Datomic.
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Let's consider a database state rollback example, representing a point in time.
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In essence, we can visualize this feature as an OS X time machine.
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On the programming side, Datomic has a Closure and Java API, along with its query engine.
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This structure makes Datomic highly scalable because we can control operations from the programming level.
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To summarize, Datomic distinguishes itself from traditional RDBMS through its handling of facts and immutability.
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Let's shift our focus back to the Diametric gem.
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The gem simplifies how we interact with Datomic, as we do not need to manually handle the schema.
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In the gem, we define classes that include the Diametric entity, and it manages the schema for us.
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Thus, integrating peer services into our Ruby applications is straightforward.
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Using the API, we can set attributes and interact with the data seamlessly.
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The Diametric gem generates dynamic transaction data to simplify data operations.
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When we create a new model instance and save it, all the transactions are managed effectively.
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In the case of updating or deleting records, the gem handles this with the concept of transactions as well.
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Next, I’ll provide a brief demonstration of working with the Diametric gem.
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First, I’ll connect to Datomic and create a schema.
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The Diametric gem generates the required schema for our application, allowing us to proceed without complications.
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The ability to specify connections and define attributes is incredibly useful.
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When querying, we can leverage the gem to simplify interaction with the data.
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For instance, the gem provides an easy way to filter results based on specific attributes.
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Furthermore, the framework supports complex queries, making it manageable to retrieve relevant entries.
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Let's talk about a more complicated example involving neighborhoods.
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In this instance, the challenge is navigating relationships between different data models.
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For example, I’m looking to identify neighborhoods based on certain characteristics, which can be straightforward with the right queries.
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The data structure allows for easy traversal up and down the relationships.
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This flexibility is one of the strengths of using Datomic with the Diametric gem.
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As such, querying for complex nested data or relationships becomes feasible.
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The capability to read all current data states, even with large datasets, further emphasizes Datomic's efficiency.
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We often deal with many results, and this performance is crucial for applications that rely on rapid data access.
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By defining schemas carefully, we can ensure seamless integration into Ruby applications.
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Returning to the original context, let’s consider the practical implementation of Datomic from Rails.
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This setup allows Ruby developers to interact with Datomic as they would with standard database systems.
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I have a working example of integrating Datomic, showcasing its functionality with Rails.
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In this project, I utilized jRuby on Rails and the Diametric gem.
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This integration serves as a valuable demonstration of how these technologies can cooperate.
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The outcome is a functional Rails application leveraging the strengths of Datomic.
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To conclude, while there are some challenges with mapping functions, the overall experience has been rewarding.
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I encourage contributions from the community to improve the gem further.
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Thank you all for attending this session, and I hope you can explore the possibilities that Datomic and the Diametric gem offer Ruby developers.
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If you have any questions or need clarification on anything discussed, feel free to ask.
