At Databricks, we used Databricks Notebooks and cluster management to set up a reproducible benchmarking harness that compares the performance of Apache Spark’s Structured Streaming, running on Databricks Unified Analytics Platform, against other open source streaming systems such as Apache Kafka Streams and Apache Flink. In particular, we used the following systems and versions in our benchmarks:
The Yahoo Streaming Benchmark is a well-known benchmark used in industry to evaluate streaming systems. When setting up our benchmark, we wanted to push each streaming system to its absolute limits, yet keep the business logic the same as in the Yahoo Streaming Benchmark. We shared some of the results we achieved from these benchmarks during Spark Summit West 2017 keynote showing that Spark can reach 5x or higher throughput over other popular streaming systems. In this blog, we discuss in more detail about how we performed this benchmark, and how you can reproduce the results yourselves.
Standard vendor-based metric warnings aside, you can get the benchmark details at their GitHub repo.
As a recent client requirement I needed to propose a solution in order to add spark2 as interpreter to zeppelin in HDP (Hortonworks Data Platform) 2.5.3
The first hurdle is, HDP 2.5.3 comes with zeppelin 0.6.0 which does not support spark2, which was included as a technical preview. Upgrade the HDP version was not an option due to the effort and platform availability. At the end I found in the HCC (Hortonworks Community Connection) a solution, which involves installing a standalone zeppelin which does not affect the Ambari managed zeppelin delivered with HDP 2.5.3.
I want to share how I did it with you.
Read on to see how Paul did it. It’s not trivial but Paul lays out the process step-by-step.
Akka gives you the opportunity to make logic for producing/consuming messages from Kafka with the Actor model. It’s very convenient if actors are widely used in your code and it significantly simplifies making data pipelines with actors. For example, you have your Akka Cluster, one part of which allows you to crawl of web pages and the other part of which makes it possible to index and send indexed data to Kafka. The consumer can aggregate this logic. Producing data to Kafka looks as follows:
The Actor model, which Akka implements, is something I kind of understand, but have never spent much time trying to implement. I can see how it’d make perfect sense communicating with Kafka, though, given the scale and independence of consumers within a consumer group that Kafka provides.
The major addition to this release is Structured Streaming. It has been marked as production ready and its experimental tag has been removed.
Some of the high-level changes and improvements :
Production ready Structured Streaming
Expanding SQL functionalities
New distributed machine learning algorithms in R
Additional Algorithms in MLlib and GraphX
Read on for more details.
Spark Offers three types of Cluster Managers :
4) Kubernetes (experimental) – In addition to the above, there is experimental support for Kubernetes. Kubernetes is an open-source platform for providing container-centric infrastructure.
Read on for a description of the top three cluster managers.
The Spark Streaming integration for Kafka 0.10 is similar in design to the 0.8 Direct Stream approach. It provides simple parallelism, 1:1 correspondence between Kafka partitions and Spark partitions, and access to offsets and metadata. However, because the newer integration uses the new Kafka consumer API instead of the simple API, there are notable differences in usage. This version of the integration is marked as experimental, so the API is potentially subject to change.
In this blog, I am going to implement the basic example on Spark Structured Streaming & Kafka Integration.
This is a code-heavy tutorial, so check it out.
Unfortunately, C&P comes in to play, therefore, if at some point in time a default value for ‘trackLength’ is also required, you may end up changing both of these methods. Another disadvantage is that if another similar method, which requires the same default values, is added, code duplication is unavoidable.
A possible solution, which helps to reduce boilerplate, is DataFrameNaFunctions, which is intended to be used for handling missing data: replacing specific values, dropping ‘null’ and ‘NaN’, and setting default values
Read on for an example.
At its core, Spark’s Catalyst optimizer is a general library for representing query plans as trees and sequentially applying a number of optimization rules to manipulate them. A majority of these optimization rules are based on heuristics, i.e., they only account for a query’s structure and ignore the properties of the data being processed, which severely limits their applicability. Let us demonstrate this with a simple example. Consider a query shown below that filters a table t1 of size 500GB and joins the output with another table t2of size 20GB. Spark implements this query using a hash join by choosing the smaller join relation as the build side (to build a hash table) and the larger relation as the probe side 1. Given that t2 is smaller than t1, Apache Spark 2.1 would choose the right side as the build side without factoring in the effect of the filter operator (which in this case filters out the majority of t1‘s records). Choosing the incorrect side as the build side often forces the system to give up on a fast hash join and turn to sort-merge join due to memory constraints.
Click through for a very interesting look at this query optimzier.
Databricks’ engineers and Apache Spark committers Matei Zaharia, Tathagata Das, Michael Armbrust and Reynold Xin expound on why streaming applications are difficult to write, and how Structured Streaming addresses all the underlying complexities.
There’s quite a bit of reading material on the other side.
This is a hands-on tutorial that can be followed along by anyone with programming experience. If your programming skills are rusty, or you are technically minded but new to programming, we have done our best to make this tutorial approachable. Still, there are a few prerequisites in terms of knowledge and tools.
The following tools will be used:
Git—to manage and clone source code
Docker—to run some services in containers
Java 8 (Oracle JDK)—programming language and a runtime (execution) environment used by Maven and Scala
Maven 3—to compile the code we write
Some kind of code editor or IDE—we used the community edition of IntelliJ while creating this tutorial
Scala—programming language that uses the Java runtime. All examples are written using Scala 2.12. Note: You do not need to download Scala.
The Hello World of streaming apps is a Twitter client.