AVRO schema generation with reusable fields

Why use AVRO and AVRO Schema?

There are several serialized file formats out there, so chosing the one most suited for your needs is crucial. This blog entry will not compare them, but it will just point out some advantages of AVRO and AVRO Schema for an Apache Hadoop ™ based system.

  • Avro schema can be written in JSON
  • Avro schema is always present with data, getting rid of the need to know the schema before accessing the data
  • small file size, since schema is always present there need to be stored less type information
  • schema evolution is possible by using a union field type with default values. This was explained here. Deleted fields also need to be defined with a default value.
  • Avro files are compressible and splitable by Hadoop MapReduce and other tools from the Hadoop universe.
  • files can be compressed with Snappy and Deflate.

AVRO Schema generation

Generating Apache AVRO ™ schemas is pretty straight forward. They can be written in JSON and are always stored with the data. There are field types for everything needed, even complex types, such as maps and arrays. A schema can also contain a record, which is in itself an independent schema, as a field. This makes it possible to store data of almost unlimited complexity in AVRO. In case of very complex schema definitions keep in mind, that to access complex data structures can be very expensive later on in the process of transforming and working with such data. Here are some examples of AVRO supported datatypes

AVRO Datatypes

  • Primitive types as null, integer, long, boolean float, double, string and byte
  • Complex types such as records. This fields are basically complete schemas in their own right. These fields consist of:
    • name
    • namespace
    • fields
  • Enums
  • Arrays
  • Maps
  • Fixed length fields
  • Logical datatypes

Logical datatypes are something special and by using these you can define other fields you might need. As you can see in the list above there is no datatype for date or datetime. These are implemented using logical datatypes. Define a logical type like this:

{
  "type": "bytes",
  "logicalType": "decimal",
  "precision": 4,
  "scale": 2
}

Supported logical datatypes are decimal, date, time, timestamp and duration.

Downsides in Schema Generation

There is one downside though, namely that individual fields are not reusable. This topic was addressed by Treselle Systems in this entry. They introduce a way to make fields in a AVRO Schema reusable by working with placeholders and then replacing them with before defined subschemas. This comes in handy when you have fields, that should be available in each AVRO schema, such as meta information for a message pushed into your system.

AVRO Schema Generator

To make AVRO Schema generation more comfortable, I worked on a project, inspired by Treselle Systems’ text and combined it with other tools I use daily:

  • Jupyter Notebook
  • AVRO-Doc: a JS based server reformatting AVRO schemas into an easily readable HTML format.
  • AVRO schema repo server: a simple REST based server to publish schemas to and provide them for all parties that generate and consume the data stored in AVRO format
AVRO Schema Generator
AVRO Schema Generator

This combination of several tools makes it possible to handle data more easily.

Schema generator

Schemas are written using a Jupyter notebook server. The project contains:

  • AVRO Schema Editor.ipynb: To create new schemas and adapt existing ones. You load the existing files into the notebook and then edit them before saving them to file again.
  • Avro Schema Generator.ipynb: This notebook checks schema syntax and replaces subschemas in a generated version of the schema. Subschemas need to be defined before generating a final version of a schema. This notebook also implents functions to upload the schemas to the repository server.
  • Docker file for setting up the schema repository server in
    docker_schema_repo. Make sure to set the correct URL before trying to upload the generated schemas.
  • Docker file for setting up the avrodoc server, with built in active directory plugin in Nginx. Find this file in
    docker_avrodoc

The project contains an example schema for reference.

Schema repository

The schema repository provides a generally available schema store. This store has a built-in version control. It helps sources to take their time adapting to a new version of the schema.

This asynchronity is possible, because all schemas are compatible with previous version. With that restraint it is also possible to have different sources push one schema in different versions and still be able to transform the data using one process. Not existing values in different version of a schema are filled with the mandatory default value and this default value can even be NULL.

Conclussion

This project aims to help managing data definitions in Hadoop based systems. With the schema repository it provides a single source of truth for all data definitions, at least for data entry and if you decide to use AVRO schemas thourghout your system, even after transformation, you can manage all data definition here.

There are several other schema repositories out there, that can be used, e.g. the one provided by Confluent or the one introduced by Hortonworks for Apache Nifi. The tools used here are just examples of how such a system can be set up and how to introduce reusable AVRO fields into your schemas.

The code can be found in our repository.

Plumber: Getting R ready for production environments?

R Project and Production

Running R Project in production is a controversially discussed topic, as is everything concerning R vs Python. Lately there have been some additions to the R Project, that made me look into this again. Researching R and its usage in production environments I came across several packages / project, that can be used as a solution for this:

There are several more, but those I found the most interesting.

Plumber


For reasons of ease of use and because it was not a hosted version, I took a deeper look into Plumber. This felt quite natural as it uses function decorators for defining endpoints and parameters. This is similar to Spring Boot, which I normally use for programming REST APIs.
So using Plumber is really straight forward, as the example below shows:

#' return text "Hello"
#' @get /hello
function() {
  list(msg = "hello")
}

The #’ @get defines the endpoint for this request. In this case /hello, so the full url on localhost is http://127.0.0.1:8001/hello. To pass in one or more parameters you can use the decorator #’ @param parameter_name parameter_description. A more complicated example using Plumber is hosted on our Gitlab. This example was built with the help of Tidy Textmining.

Production ready?

Plumber comes with Swagger, so the webserver is automatically available. As the R instance is already running, processing the R code does not take long. If your model is complicated, then, of course, this is reflected in the processing time. But as R is a single thread programming language, Plumber can only process one request at a time.
There are ways to tweak this of course. You can run several instances of the service, using a Docker image. This is decribed here. There is also the option of using a webserver to fork the request to serveral instances of R. Depending on the need of the API, single thread processing can be fast enough. If the service has to be highly available the Docker solutions seems like a good choice, as it comes with a load balancer.

Conclussion

After testing Plumber I am surprised by the easy of use. This package makes deploying an REST API in R really easy. Depending on your business needs, it might even be enough for a productive scenario, especially when used in combination with Docker.

Analytics Platform: An Evolution from Data Lake

Analytics Platform

Having built a Data Lake for your company’s analytical needs, there soon will arise new use cases, that cannot be easily covered with the Data Lake architecture I covered in previous posts, like Apache HAWQ™: Building an easily accessable Data Lake. You will need to adapt or enhance your architecture to become more flexible. One way to make this flexibility happens, is to transform your Data Lake into an Analytics Platform.

Definition of an Analytics Platform

An Analytics Platform is a platform that provides all kind of services needed for building data products. This often exceeds the functionality of a pure RDBMS or even a Data Lake based on Apache HAWQ™. There are data products that have more requirements than a SQL inferface. Reporting and basic analysis are addressed by this setup, but products dealing with predictions or recommendations often have different needs. An Analytics Platform provides flexibility in the tools used. There can be, for example, a Apache HAWQ™ setup and at the same time an environment for running Tensorflow applications.

Using existing parts: Multi-colored YARN

When you are running a Hadoop Cluster™, you are already familiar with a resource manager. This manager is YARN. With YARN you can already deploy Linux Containers, and support for Docker containers has already progressed pretty far (YARN-3611). Building complex applications and managing them with YARN is called Multi-colored YARN by Hortonworks.
Following through on this idea you will have a cluster with just some central services installed directly on bare metal. You will deploy the rest in containers, as shown in the images below.

Analytics Platform
Analytics Platform based on YARN and Docker

The example makes use of Kubernetes and Docker for virtualization and provides the following services on bare meta, since they are needed by most applications:

  • Ambari
  • Kuberneted
  • YARN
  • ZooKeeper
  • HDFS

Especially the HDFS is important as a central services. This makes it possible for all applications to access the some data. The Picture above shows, that there can be several instances of a Hadoop distribution. This is possible even in different version. So the platform allows for multi tenancy, while all instances are still processing the same data.

Development changes

Having an Analytics Platform makes the development of data products easier. There always was the problem of developing a product on a sample of the data, when you used development and staging systems, as decribed by me here. In same cases these did not contain all possibly combinations of data. This could result in error after a deployemnt on the production environment. Even going through all development and staging could not change this. This new approach allows you to deploy all three systems on the same data. So there you can account for all data induced errors on the development and staging systems already.
You can even become more agile in your development process. The picture below shows an example deployment process, that uses this system.

Analytics Platform: Deployment Process

Conclussion

Moving from a pure Data Lake to an Analytics Platform give you are flexibility and helps in the development of data products. Especially since you can develop on the same data as is available on production. Of course it brings more complexity to an already complex environment. But since it is possible to keep YARN as resource manager and move to a more agile way of development and deployment, it might be worth considering. Once Multi-Colored YARN is finished, it will be easier to make this happen.