Press "Enter" to skip to content

Category: Spark

Delta Lake and ACID Properties

Published 2020-02-06 by Kevin Feasel

Kundan Kumarr notes that Spark’s Delta Lake allows for ACID transactions:

DeltaLog is the crux of Delta Lake which ensures atomicity, consistency, isolation, and durability of user-initiated transactions. DeltaLog is an ordered record of transactions. Every transaction performed since the inception of Delta Lake Table, has an entry in the DeltaLog (also known as the Delta Lake transaction log). It acts as a single source of truth, giving users access to the last version of a DeltaTable’s state. It provides serializability, the strongest level of isolation level. Let’s see how DeltaLog ensures ACID Transactions.

Click through for the explanation.

Comments closed

Quick Hits on Azure Databricks Performance

Published 2020-02-05 by Kevin Feasel

Rayis Imayev has a few thoughts on optimizing delta table-based workloads in Azure Databricks:

2) Enable the Delta cache – spark.databricks.io.cache.enabledtrue
There is a very good resource available on configuring this Spark config setting: https://docs.microsoft.com/en-us/azure/databricks/delta/optimizations/delta-cache

And this will be very helpful in your Databricks notebook’s queries when you try to access a similar dataset multiple times. Once you read this dataset for the first time, Spark places it into internal local storage cache and will speed up the process of further referencing it for you.

Click through for several more along these lines.

Comments closed

What’s New with Delta Lake

Published 2020-01-30 by Kevin Feasel

Denny Lee and Tathagata Das announce Delta Lake 0.5.0:

With the following pull requests, you can now run even more Delta Lake operations concurrently. With finer grain conflict detection, these updates make it easier to run complex workflows on Delta tables such as:

– Running deletes (e.g. for GDPR compliance) concurrently on older partitions while newer partitions are being appended.
– Running file compactions concurrently with appends.
– Running updates and merges concurrently on disjoint sets of partitions.

Click through for the full changelog.

Comments closed

Spark is Not ACID Compliant

Published 2020-01-27 by Kevin Feasel

Kundan Kumarr explains how it is that Apache Spark is not ACID compliant:

Atomicity states that it should either write full data or nothing to the data source when using spark data frame writer. Consistency, on the other hand, ensures that the data is always in a valid state.

As evident from the spark documentation below, it is clear that while saving data frame to a data source, existing data will be deleted before writing the new data. But in case of job failure, the original data will be lost or corrupted and no new data will be written.

Click through for an explanation of these two along with a demo, and then an explanation of how Spark Datasets don’t follow the Isolation or Durability properties either. I don’t think any of this is earth-shattering to people, but it is a good reminder that Spark doesn’t fit all use cases.

Comments closed

Improving Join Performance on ADF Data Flows

Published 2020-01-27 by Kevin Feasel

Mark Kromer has a few tips on improving ADF data flow join performance:

When you include literal values in your join conditions, Spark may see that as a requirement to perform a full cartesian product first, then filter out the joined values. But if you ensure that you (1) have column values from both sides of your join condition, you can avoid this Spark-induced cartesian product and improve the performance of your joins and data flows. (2) Avoid use of literal conditions to represent the results of one side of your join condition.

In other words, avoid this for your join condition:source1@movieId == '1'Instead, implement that with a dummy derived column. 

There are several good tips in this post.

Comments closed

Using Koalas on Azure Databricks

Published 2020-01-21 by Kevin Feasel

Ginger Grant shows how you can install the koalas library on an Azure Databricks cluster:

Unfortunately if you are using an ML workspace, this will not work and you will get the error message org.apache.spark.SparkException: Library utilities are not available on Databricks Runtime for Machine Learning. The Koalas github documentation  says “In the future, we will package Koalas out-of-the-box in both the regular Databricks Runtime and Databricks Runtime for Machine Learning”.  What this means is if you want to use it now

Most of the time I want to install on the whole cluster as I segment libraries by cluster.  This way if I want those libraries I just connect to the cluster that has them. Now the easiest way to install a library is to open up a running Databricks cluster (start it if it is not running) then go to the Libraries tab at the top of the screen.

Click through for a demo of what you need to do.

Comments closed

Databricks Automated Deployment and Testing

Published 2020-01-17 by Kevin Feasel

Li Yu, et al, explain how to use Databricks notebooks and MLflow to automate deployment and testing of Spark solutions:

Today many data science (DS) organizations are accelerating the agile analytics development process using Databricks notebooks.  Fully leveraging the distributed computing power of Apache Spark™, these organizations are able to interact easily with data at multi-terabytes scale, from exploration to fast prototype and all the way to productionize sophisticated machine learning (ML) models.  As fast iteration is achieved at high velocity, what has become increasingly evident is that it is non-trivial to manage the DS life cycle for efficiency, reproducibility, and high-quality. The challenge multiplies in large enterprises where data volume grows exponentially, the expectation of ROI is high on getting business value from data, and cross-functional collaborations are common.

In this blog, we introduce a joint work with Iterable that hardens the DS process with best practices from software development.  This approach automates building, testing, and deployment of DS workflow from inside Databricks notebooks and integrates fully with MLflow and Databricks CLI. It enables proper version control and comprehensive logging of important metrics, including functional and integration tests, model performance metrics, and data lineage. All of these are achieved without the need to maintain a separate build server.

Read on to see how.

Comments closed

Spark on Docker on YARN on Cloud

Published 2020-01-09 by Kevin Feasel

Adam Antal has included all of the layers:

Bringing your own libraries to run a Spark job on a shared YARN cluster can be a huge pain. In the past, you had to install the dependencies independently on each host or use different Python package management softwares. Nowadays Docker provides a much simpler way of packaging and managing dependencies so users can easily share a cluster without running into each other, or waiting for central IT to install packages on every node. Today, we are excited to announce the preview of Spark on Docker on YARN available on CDP DataCenter 1.0 release.

Joking about stack length aside, this looks really useful.

Comments closed

Accessing S3 Data from Apache Spark

Published 2020-01-08 by Kevin Feasel

Divyansh Jain shows how we can connect to AWS’s S3 using Apache Spark:

Now, coming to the actual topic that how to read data from S3 bucket to Spark. Well, it is not very easy to read S3 bucket by just adding Spark-core dependencies to your Spark project and use spark.read to read you data from S3 Bucket.

So, to read data from an S3, below are the steps to be followed:

This isn’t a built-in source, so there is a little bit of work to do, but it’s not that bad.

Comments closed

Controlling IoT Devices via Databricks

Published 2020-01-06 by Kevin Feasel

Saeed Barghi takes us through building an interesting solution:

A few weeks ago I did a talk at AI Bootcamp here in Melbourne on how we can build a serverless solution on Azure that would take us one step closer to powering industrial machines with AI, using the same technology stack that is typically used to deliver IoT analytics use cases. I demoed a solution that received data from an IoT device, in this case a crane, compared the data with the result of a machine learning model that has ran and written its predictions to a repository, in this case a CSV file, and then decided if any actions needs to be taken on the machine, e.g. slowing the crane down if the wind picks up.

This was a really interesting article.

Comments closed