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Category: Hadoop

Serverless Lambda Architecture

Published 2017-09-12 by Kevin Feasel

Laith Al-Saadoon shows off a new Amazon Web Services product, AWS Glue, which allows you to build a data processing system on the Lambda architecture without directly provisioning any EC2 instances:

With the launch of AWS Glue, AWS provides a portfolio of services to architect a Big Data platform without managing any servers or clusters. AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (for example, the table definition and schema) in the AWS Glue Data Catalog. After it’s cataloged, your data is immediately searchable, queryable, and available for ETL.

AWS Glue generates the code to execute your data transformations and data loading processes. Furthermore, AWS Glue provides a managed Spark execution environment to run ETL jobs against a data lake in Amazon S3. In short, you can now run a Lambda Architecture in AWS in a completely 100% serverless fashion!

“Serverless” applications allow you to build and run applications without thinking about servers. What this means is that you can now stream data in real-time, process huge volumes of data in S3, and run SQL queries and visualizations against that data without managing server provisioning, installation, patching, or capacity scaling. This frees up your users to spend more time interpreting the data and deriving business value for your organization.

Laith has a working demo of the process available as well.

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Message Transformation Within Kafka

Published 2017-09-08 by Kevin Feasel

Robin Moffatt shows how to use Single Message Transforms inside Kafka Connect to reshape messages as you send them downstream:

Single Message Transforms (SMT) is a functionality within Kafka Connect that enables the transformation … of single messages. Clever naming, right?! Anything that’s more complex, such as aggregating or joins streams of data should be done with Kafka Streams — but simple transformations can be done within Kafka Connect itself, without needing a single line of code.

SMTs are applied to messages as they flow through Kafka Connect; inbound it modifies the message before it hits Kafka, outbound and the message in Kafka remains untouched but the data landed downstream is modified.

There’s quite a bit you can do with this, so check it out.

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How The New York Times Uses Apache Kafka

Published 2017-09-07 by Kevin Feasel

Boerge Svingen gives us an architectural overview of how the New York Times uses Apache Kafka to link different services together:

These are all sources of what we call published content. This is content that has been written, edited, and that is considered ready for public consumption.

On the other side we have a wide range of services and applications that need access to this published content — there are search engines, personalization services, feed generators, as well as all the different front-end applications, like the website and the native apps. Whenever an asset is published, it should be made available to all these systems with very low latency — this is news, after all — and without data loss.

This article describes a new approach we developed to solving this problem, based on a log-based architecture powered by Apache KafkaTM. We call it the Publishing Pipeline. The focus of the article will be on back-end systems. Specifically, we will cover how Kafka is used for storing all the articles ever published by The New York Times, and how Kafka and the Streams API is used to feed published content in real-time to the various applications and systems that make it available to our readers.  The new architecture is summarized in the diagram below, and we will deep-dive into the architecture in the remainder of this article.

This is a nice write-up of a real-world use case for Kafka.

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Troubleshooting Ambari Server

Published 2017-09-06 by Kevin Feasel

Jay SenSharma has an interesting article on troubleshooting Ambari Server:

When we notice that the ambari server is responding slow then we should look first the following details first:

1). The number of hosts added to the ambari cluster. So that accordingly we can tune the ambari agent thread pools.

2). The number of concurrent users (or the view users) who access the ambari server at a time. Sothat accordingly we can tune the ambari thread pools.

3). The age of the ambari cluster. If the ambari server is too old then the possibility is that some of the operational logs and the alert histories will be consuming a large amount of the Database which might be causing ambari DB queries to respond slow.

4). The Ambari Database health and it’s geographic location from the ambari server, to isolate if there are any network delays.

5). Ambari server memory related tuning parameters to see if the ambari heap is set correctly.

6). For ambari UI slowness we should check the network proxy issues to see if there are any network proxies added between client the ambari server machine Or the network slowness.

7). If the ambari users are synced with the AD or external LDAP and if the communication between server and the AD/LDAP is good.

8). Also the resource availability on the ambari host like the available free memory and if any other service/component running on ambari server is consuming more Memory/CPU/IO.

There is a lot of detail here, including quite a few checks to run.

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Diving Into Spark’s Cost-Based Optimizer

Published 2017-09-05 by Kevin Feasel

Ron Hu, et al, explain how Spark’s cost-based optimizer works:

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.

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Connecting To Kafka Via SSL

Published 2017-09-05 by Kevin Feasel

Harikiran Nayak shows how to work with secure Kafka connections:

First, the Kafka broker must be configured to accept client connections over SSL. Please refer to the Apache Kafka Documentation to configure your broker. If your Kafka cluster is already SSL-enabled, you can look up the port number in your Kafka broker configuration file (or the broker logs). Look for the listeners=SSL://host.name:port configuration option. To ensure that the Kafka broker is correctly configured to accept SSL connections, run the following command from the same host that you are running SDC on. If SDC is running from within a docker container, log in to that docker container and run the command.

Read on for more.

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Kafka Cruise Control

Published 2017-09-01 by Kevin Feasel

Jiangjie Qin announces Cruise Control, an automated workload management system for Kafka:

Intelligent automation is critical under these circumstances, which is why we developed Cruise Control: a general-purpose system that continually monitors our clusters and automatically adjusts the resources allocated to them to meet pre-defined performance goals. In essence, users specify goals, Cruise Control monitors for violations of these goals, analyzes the existing workload on the cluster, and automatically executes administrative operations to satisfy those goals. You can see a video here about Cruise Control at the Stream Processing Meet Up last fall.

Today we are pleased to announce that we have open sourced Cruise Control and it is now available on Github. In this post, we’ll describe Cruise Control’s uses both generally and at LinkedIn, its architecture, and some unique challenges we faced when creating it. For further details about Kafka terminology used throughout this post, this reference can be a helpful guide.

This isn’t a monitoring tool per se, but rather a resource balancing tool.  And it’s now freely available to all.

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Lambda And Kappa Architectures

Published 2017-08-31 by Kevin Feasel

Michael Verrilli has a post contrasting the Lambda and Kappa data architectures:

Any query may get a complete picture by retrieving data from both the batch views and the real-time views. The queries will get the best of both worlds. The batch views may be processed with more complex or expensive rules and may have better data quality and less skew, while the real-time views give you up to the moment access to the latest possible data. As time goes on, real-time data expires and is replaced with data in the batch views.

One additional benefit to this architecture is that you can replay the same incoming data and produce new views in case code or formula changes.

The biggest detraction to this architecture has been the need to maintain two distinct (and possibly complex) systems to generate both batch and speed layers. Luckily with Spark Streaming (abstraction layer) or Talend (Spark Batch and Streaming code generator), this has become far less of an issue… although the operational burden still exists.

I haven’t seen much on the topic of Big Data architectures this year; it seems like it was a much more popular topic last year.

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KSQL: Streaming SQL For Kafka

Published 2017-08-29 by Kevin Feasel

Neha Narkhende announces KSQL:

I’m really excited to announce KSQL, a streaming SQL engine for Apache KafkaTM. KSQL lowers the entry bar to the world of stream processing, providing a simple and completely interactive SQL interface for processing data in Kafka. You no longer need to write code in a programming language such as Java or Python! KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time. It supports a wide range of powerful stream processing operations including aggregations, joins, windowing, sessionization, and much more.

Feasel’s Law wins again.  The syntax looks pretty similar to Spark Streaming and Stream Analytics, so if you get those, you’ll get this.

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Monitoring Kafka Lag

Published 2017-08-29 by Kevin Feasel

Bas Harenslak explains how to monitor consumer lag in Kafka:

So you’ve written e.g. a Spark ETL pipeline reading from a Kafka topic. There are several options for storing the topic offsets to keep track of which offset was last read. One of them is storing the offsets in Kafka itself, which will be stored in an internal topic __consumer_offsets. If you’re using the Kafka Consumer API (introduced in Kafka 0.9), your consumer will be managed in a consumer group, and you will be able to read the offsets with a Bash utility script supplied with the Kafka binaries.

The Prometheus mentioned in the article is an open-source monitoring solution.

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