Working With The Databricks API Via Powershell

Gerhard Brueckl has a Powershell module for interacting with Databricks, either Azure or AWS:

As most of our deployments use PowerShell I wrote some cmdlets to easily work with the Databricks API in my scripts. These included managing clusters (create, start, stop, …), deploying content/notebooks, adding secrets, executing jobs/notebooks, etc. After some time I ended up having 20+ single scripts which was not really maintainable any more. So I packed them into a PowerShell module and also published it to the PowerShell Gallery (https://www.powershellgallery.com/packages/DatabricksPS) for everyone to use!

This looks like a pretty good module if you work with Databricks.

Tuning Apache Spark Applications

Vidisha Gupta has a few tips for tuning Apache Spark programs:

Data Serialization – Serialization plays an important role in increasing the performance of any application. Spark provides two serialization libraries –

  • Java Serialization: By default, spark uses Java’s ObjectOutputStream framework which can work with any class that implements java.io.serializable. This serialization is flexible but slow and creates large serialized formats for many classes.

  • Kryo Serialization: Spark can use Kryo library to serialize objects. It is much faster and compact but does not support all serializable types. So we must register those classes which we want to be serialized. Therefore, Kryo uses indices instead of full class names to identify data types which reduce the size of the serialized data thereby increasing performance. We can initialize our spark conf by setting the value of the property spark.serializer to org.apache.spark.serializer.KryoSerializer. This serializer has a major impact on performance when we are shuffling or caching a large amount of data. To know more about this serializer, refer  Kryo documentation

There are some good tips in here.

Game Theory With Apache Spark

Konor Unyelioglu has a four-part series on solving game theoretical problems with Apache Spark.  Part one lays out the scenario:

One application of game theory is finding optimal resource allocation. For example, as discussed in this article, resource management for heterogeneous wireless networks involves sharing network links, e.g. 3G, Wi-Fi, WiMAX, LTE, between mobile devices of different types and different bandwidth needs. In such environments, game theory algorithms can be effectively used to decide which devices must be allocated to which network resources. Similarly, game theory can be used for allocation of cloud computing resources, e.g. CPU, storage, memory or network bandwidth, between resource clients, as discussed in this article (also see here). The concept of Mobile Edge Computing, where mobile devices offload computationally intensive tasks to the small scale computing servers located in the network edge, could utilize game theory concepts for resource allocation, as studied here.

Using game theory for resource allocation is not limited to cloud computing or telecommunications. For example, in a recent study, a technique was developed based on game theory for efficient distribution of water supply to consumers. Optimum decision making for traffic flow control at major traffic intersections can also be modeled using concepts from game theory, as studied in this article.

Part two defines an algorithm for maximizing utility given the finite set of resources:

Consider Qi(P) defined previously for i = 1, …, N. Let Qi1(P) be defined as the K-dimensional vector where the j-th entry is 1 if and only if there exists an element in Qi(P) where the j-th entry is greater than 0, j = 1,…, K. In other words, if the j-th entry of Qi1(P) is 0 then for every element in Qi(P) the j-th entry must be 0; if the j-th entry of Qi1(P) is 1 then for at least one element in Qi(P) the j-th entry must be 1.

Part 1 starts with the initial price vector at 0, i.e. P = 0, and then at each iterative step finds a new price vector, built on the previous one, that minimizes C(P). At each step, the newly constructed price vector is guaranteed to be no less than the previous one. When the price vector no longer increases, i.e. the newly constructed and previous price vectors are equal, the optimal price Po has been reached. Along with Po we also obtain Qi1(Po), i = 1, …, N, which we call optimal assignments. If the j-th entry of Qi1(Po) = 0 then agent i will not be allocated any units of resource type j. On the other hand, if the j-th entry of Qi1(Po) = 1 then agent i may be allocated some units of resource type j in Part 2 of the algorithm, although not necessarily.

Part three lays out some helper methods for solving the problem in Spark:

For an agent i, the method getMaxUtility() below calculates Vi(P) at price P, i.e. it solves the maximization problem:

max x ∈ Xi {Ui(x) – P * x}

where Xi is the consumption set of the agent.

Recall that

  • Ui = [ui1 ui2 … uiK]T
  • Ui(x) = UiT  * x = ∑ j = 1, 2, …, K  (uij * xij)
  • Ui(x) – P * x = ∑ j = 1, 2, …, K  (uij – pj)* xij

Part four shows us the code for the solution and wraps up:

In this article, we discussed an algorithm based on game theory for optimal resource allocation. The algorithm provides a fairness-based equilibrium where every agent (bidder) maximizes its utility and the resource manager (auctioneer) maximizes the price of the resources it is allocating. In addition, all the available units are allocated across all resource types and no agent is forced to take more than it is willing to. The algorithm is based on economist Ausubel’s Efficient Dynamic Auction Method.

We showed via two examples that the algorithm can be applied to different types of resource allocation problems. In one example, we applied the algorithm to allocate cloud computing resources, e.g. CPU, memory, bandwidth, to computing clients. Secondly, we applied the algorithm to a logistics example where various types of goods are transported over shared transportation resources.

If you were to create a parlor game around things guaranteed to show up in Curated SQL, “Game theory with Apache Spark” is way up on the list.  If somebody does a post combining Apache Kafka with agorics, that’s an instant link too.

Quick Spark Notes

Leela Prasad has a few quick notes on concepts in Apache Spark:

Broadcast Variables

Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables using efficient broadcast algorithms to reduce communication cost.
Spark actions are executed through a set of stages, separated by distributed “shuffle” operations. Spark automatically broadcasts the common data needed by tasks within each stage. The data broadcasted this way is cached in serialized form and deserialized before running each task. This means that explicitly creating broadcast variables is only useful when tasks across multiple stages need the same data or when caching the data in deserialized form is important.

There’s some good stuff on accumulators and the SparkSession object in there as well.

Azure Databricks Geospatial Analysis

Jose Mendes gives us an example of using Azure Databricks to perform geospatial analysis:

Magellan is a distributed execution engine for geospatial analytics on big data. It is implemented on top of Apache Spark and deeply leverages modern database techniques like efficient data layout, code generation and query optimization in order to optimize geospatial queries (further details here).

Although people mentioned in their GitHub page that the 1.0.5 Magellan library is available for Apache Spark 2.3+ clusters, I learned through a very difficult process that the only way to make it work in Azure Databricks is if you have an Apache Spark 2.2.1 cluster with Scala 2.11. The cluster I used for this experience consisted of a Standard_DS3_v2 driver type with 14GB Memory, 4 Cores and auto scaling enabled.

In terms of datasets, I used the NYC Taxicab dataset to create the geometry points and the Magellan NYC Neighbourhoods GeoJSON dataset to extract the polygons. Both datasets were stored in a blob storage and added to Azure Databricks as a mount point.

It sounds like this is much faster than using U-SQL to perform the same task.

Using Datadog To Monitor Spark Clusters On EMR

Priya Matpadi walks us through one way to monitor Spark clusters on Amazon ElasticMapReduce:

We recently implemented a Spark streaming application, which consumes data from from multiple Kafka topics. The data consumed from Kafka comprises different types of telemetry events generated by mobile devices. We decided to host the Spark cluster using the Amazon EMR service, which manages a fleet of EC2 instances to run our data-processing pipelines.

As part of preparing the cluster and application for deployment to production, we needed to implement monitoring so we could track the streaming application and the Spark infrastructure itself. At a high level, we wanted ensure that we could monitor the different components of the application, understand performance parameters, and get alerted when things go wrong.

In this post, we’ll walk through how we aggregated relevant metrics in Datadog from our Spark streaming application running on a YARN cluster in EMR.

Check it out.  If this is interesting, Priya’s blog has the full series.

Pivoting With Spark SQL

MaryAnn Xue shows us how to use the PIVOT operator in Spark SQL:

Pivot was first introduced in Apache Spark 1.6 as a new DataFrame feature that allows users to rotate a table-valued expression by turning the unique values from one column into individual columns.

The upcoming Apache Spark 2.4 release extends this powerful functionality of pivoting data to our SQL users as well. In this blog, using temperatures recordings in Seattle, we’ll show how we can use this common SQL Pivot feature to achieve complex data transformations.

The syntax is quite similar to the PIVOT syntax that SQL Server uses.

Logistic Regression With Apache Spark

Manoj Gautam shows how to perform a logistic regression with Apache Spark:

Since we are going to try algorithms like Logistic Regression, we will have to convert the categorical variables in the dataset into numeric variables. There are 2 ways we can do this.

  1. Category Indexing
  2. One-Hot Encoding

Here, we will use a combination of StringIndexer and OneHotEncoderEstimator to convert the categorical variables. The OneHotEncoderEstimator will return a SparseVector.

Click through for the code and explanation.

Change Data Capture With Databricks Delta

Ameet Kini and Denny Lee show how to use Databricks Delta to handle change data capture from different processes:

With Databricks Delta, the CDC pipeline is now streamlined and can be refreshed more frequently: Informatica => S3 => Spark Hourly Batch Job => Delta. In this scenario, Informatica writes change sets directly to S3 using Informatica’s Parquet writer. Databricks jobs run at the desired sub-nightly refresh rate (e.g., every 15 min, hourly, every 3 hours, etc.) to read these change sets and update the target Databricks Delta table.

With minor changes, this pipeline has also been adapted to read CDC records from Kafka, so the pipeline there would look like Kafka => Spark => Delta. In the rest of this section, we elaborate on this process, and how we use Databricks Delta as a sink for their CDC workflows.

With one of our customers, we implemented these CDC techniques on their largest and most frequently refreshed ETL pipeline. In this customer scenario, Informatica writes a change set to S3 for each of its 65 tables that have any changes every 15 minutes.   While the change sets themselves are fairly small (< 1000 records), their target tables can become quite large. Out of the 65 tables, roughly half a dozen are in the 50m-100m row range, and the rest are smaller than 50m. In Oracle, this pipeline would have run every 15 minutes, keeping in sync with Informatica. In Databricks Delta, we thought this would take close to an hour due to S3 latencies but ended up being pleasantly surprised with a 30 and even 15-minute refresh period depending on cluster size.

Click through for the rest of the story.

Using IO Cache To Speed Up Spark Jobs

Chris Seferlis looks at what the HDInsight team has done to speed up Apache Spark jobs:

The big news here is the recently released preview of HDInsight IO Cache, which is a new transparent data caching feature that provides customers with up to 9X performance improvement for Spark jobs, without an increase in costs.

There are many open source caching products that exist in the ecosystem: Alluxio, Ignite, and RubiX to name a few big ones. The IO Cache is also based on RubiX and what differentiates RubiX from other comparable caching products is its approach of using SSD and eliminating the need for explicit memory management. While other comparable caching products leverage the reservation of operating memory for caching the data.

Read on for more details.

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