Category: Streaming

I start a new series on Apache Kafka:

The broker serves several purposes:

1. Know who the producers are and who the consumers are.  This way, the producers don’t care who exactly consumes a message and aren’t responsible for the message after they hand it off.
2. Buffer for performance.  If the consumers are a little slow at the moment but don’t usually get overwhelmed, that’s okay—messages can sit with the broker until the consumer is ready to fetch.
3. Let us scale out more easily.  Need to add more producers?  That’s fine—tell the broker who they are.  Need to add consumers?  Same thing.
4. What about when a consumer goes down?  That’s the same as problem #2:  hold their messages until they’re ready again.

So brokers add a bit of complexity, but they solve some important problems.  The nice part about a broker is that it doesn’t need to know anything about the messages, only who is supposed to receive it.

This is an introduction to the product and part one of an eight-part series.

David Brinegar discusses consumer groups and lag in Apache Kafka:

While the Consumer Group uses the broker APIs, it is more of an application pattern or a set of behaviors embedded into your application.  The Kafka brokers are an important part of the puzzle but do not provide the Consumer Group behavior directly.  A Consumer Group based application may run on several nodes, and when they start up they coordinate with each other in order to split up the work.  This is slightly imperfect because the work, in this case, is a set of partitions defined by the Producer.  Each Consumer node can read a partition and one can split up the partitions to match the number of consumer nodes as needed.  If the number of Consumer Group nodes is more than the number of partitions, the excess nodes remain idle. This might be desirable to handle failover.  If there are more partitions than Consumer Group nodes, then some nodes will be reading more than one partition.

Read the whole thing.  It’s part one of a series.

Timothy Spann ties together a bunch of interesting things with Apache Nifi, including integrations with Twitter, Slack, Tensorflow, and Zeppelin:

First up, I used GetTwitter to read tweets and filtered on these terms:

strata, stratahadoop, strataconf, NIFI, FutureOfData, ApacheNiFi, Hortonworks, Hadoop, ApacheHive, HBase, ApacheSpark, ApacheTez, MachineLearning, ApachePhoenix, ApacheCalcite,ApacheStorm, ApacheAtlas, ApacheKnox, Apache Ranger, HDFS, Apache Pig, Accumulo, Apache Flume, Sqoop, Apache Falcon

Input:

InvokeHttp: I used this to download the first image URL from tweets.

It’s interesting to see this all tie together relatively easily.

Chris Marshall shows how to perform anomaly detection using AWS Kinesis Analytics:

The RANDOM_CUT_FOREST function greatly simplifies the programming required for anomaly detection.  However, understanding your data domain is paramount when performing data analytics.  The RANDOM_CUT_FOREST function is a tool for data scientists, not a replacement for them.  Knowing whether your data is logarithmic, circadian rhythmic, linear, etc. will provide the insights necessary to select the right parameters for RANDOM_CUT_FOREST.  For more information about parameters, see the RANDOM_CUT_FOREST Function.

Fortunately, the default values work in a wide variety of cases. In this case, use the default values for all but the subSampleSize parameter.  Typically, you would use a larger sample size to increase the pool of random samples used to calculate the anomaly score; for this post, use 12 samples so as to start evaluating the anomaly scores sooner.

Your SQL query outputs one record every ten seconds from the tumbling window so you’ll have enough evaluation values after two minutes to start calculating the anomaly score.  You are also using a cutoff value where records are only output to “DESTINATION_SQL_STREAM” if the anomaly score from the function is greater than 2 using the WHERE clause. To help visualize the cutoff point, here are the data points from a few runs through the pipeline using the sample Python script:

This kind of scenario is pretty cool—you could also do things like detecting service outages in streams (fewer than X events in a window, where X is some very small number relative to your overall data) or changes in advertising campaigns.

Neha Narkhede and Stephan Ewen compare Apache Flink versus Kafka Streams:

Before Flink, users of stream processing frameworks had to make hard choices and trade off either latency, throughput, or result accuracy. Flink was the first open source framework (and still the only one), that has been demonstrated to deliver (1) throughput in the order oftens of millions of events per second in moderate clusters, (2) sub-second latency that can be as low as few 10s of milliseconds, (3) guaranteed exactly once semantics for application state, as well as exactly once end-to-end delivery with supported sources and sinks (e.g., pipelines from Kafka to Flink to HDFS or Cassandra), and (4) accurate results in the presence of out of order data arrival through its support for event time. Flink is based on a cluster architecture with master and worker nodes. Flink clusters are highly available, and can be deployed standalone or with resource managers such as YARN and Mesos. This architecture is what allows Flink to use a lightweight checkpointing mechanism to guarantee exactly-once results in the case of failures, as well allow easy and correct re-processing via savepoints without sacrificing latency or throughput. Finally, Flink is also a full-fledged batch processing framework, and, in addition to its DataStream and DataSet APIs (for stream and batch processing respectively), offers a variety of higher-level APIs and libraries, such as CEP (for Complex Event Processing), SQL and Table (for structured streams and tables), FlinkML (for Machine Learning), and Gelly (for graph processing). Flink has been proven to run very robustly in production at very large scale by several companies, powering applications that are used every day by end customers.

The upshot is that the two products don’t do exactly the same thing, and there might be room in your organization for the two of them.