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:portconfiguration 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.
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.
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.
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.
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.
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.
The most common SCD update strategies are:
Type 1: Overwrite old data with new data. The advantage of this approach is that it is extremely simple, and is used any time you want an easy to synchronize reporting systems with operational systems. The disadvantage is you lose history any time you do an update.
Type 2: Add new rows with version history. The advantage of this approach is that it allows you to track full history. The disadvantage is that your dimension tables grow without limit and may become very large. When you use Type 2 SCD you will also usually need to create additional reporting views to simplify the process of seeing only the latest dimension values.
Type 3: Add new rows and manage limited version history. The advantage of Type 3 is that you get some version history, but the dimension tables remain at the same size as the source system. You also won’t need to create additional reporting views. The disadvantage is you get limited version history, usually only covering the most recent 2 or 3 changes.
The Hive solution is getting closer and closer to a traditional relational warehouse solution. And on the whole, that’s a good thing.
Whilst Kafka Connect is part of Apache Kafka itself, if you want to stream data from Kafka to Elasticsearch you’ll want the Confluent Open Source distribution (or at least, the Elasticsearch connector).
The configuration is pretty simple. As before, see inline comments for details
It’s worth noting that if you’re using the same convertor throughout your pipelines (Avro, in this case) you’d actually put this in the Connect worker config itself rather than repeating it for each connector configuration.
This is a simple example which shows just how easy it can be.
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.
The primary form of strong authentication used on a secure cluster is Kerberos. Kerberos supports credentials delegation where a server process to which a user has authenticated, can perform actions on behalf of the user. This involves the server process accessing databases or other web services as the authenticated user. Historically the form of delegation that was supported by Kerberos is now called “full delegation”. In this type of delegation, the Ticket Granting Ticket (TGT) of the user is made available to the server process and server can then authenticate to any service where the user has been granted authorization. Until recently most Kerberos Key Distribution Center(KDC)s other than Active Directory supported only this form of delegation. Also Java until Java 7 supported only this form of delegation. Starting with Java 8, Java now supports Kerberos constrained delegation (S4U2Proxy), where if the KDC supports it, it is possible to specify which particular services the server process can be delegated access to.
Hadoop within its security framework has implemented impersonation or proxy support that is independent of Kerberos delegation. With Hadoop impersonation support you can assign certain accounts proxy privileges where the proxy accounts can access Hadoop resources or run jobs on behalf of other users. We can restrict proxy privileges granted to a proxy account to act on behalf of only certain users who are members of certain groups and/or only for connections originating from certain hosts. However we can’t restrict the proxy privileges to only certain services within the cluster.
What we are discussing in this article is how to setup Kerberos constrained delegation and access a secure cluster. The example here involves Apache Tomcat, however you can easily extend this to other Java Application Servers.
This is a good article showing specific details on using Kerberos in applications connecting to Hadoop.