About This Book
- Construct a series of Flume agents using the Apache Flume service to efficiently collect, aggregate, and move large amounts of event data
- Configure failover paths and load balancing to remove single points of failure
- Use this step-by-step guide to stream logs from application servers to Hadoop's HDFS
Who This Book Is For
If you are a Hadoop programmer who wants to learn about Flume to be able to move datasets into Hadoop in a timely and replicable manner, then this book is ideal for you. No prior knowledge about Apache Flume is necessary, but a basic knowledge of Hadoop and the Hadoop File System (HDFS) is assumed.
What You Will Learn
- Understand the Flume architecture, and also how to download and install open source Flume from Apache
- Follow along a detailed example of transporting weblogs in Near Real Time (NRT) to Kibana/Elasticsearch and archival in HDFS
- Learn tips and tricks for transporting logs and data in your production environment
- Understand and configure the Hadoop File System (HDFS) Sink
- Use a morphline-backed Sink to feed data into Solr
- Create redundant data flows using sink groups
- Configure and use various sources to ingest data
- Inspect data records and move them between multiple destinations based on payload content
- Transform data en-route to Hadoop and monitor your data flows
In Detail
Apache Flume is a distributed, reliable, and available service used to efficiently collect, aggregate, and move large amounts of log data. It is used to stream logs from application servers to HDFS for ad hoc analysis.
This book starts with an architectural overview of Flume and its logical components. It explores channels, sinks, and sink processors, followed by sources and channels. By the end of this book, you will be fully equipped to construct a series of Flume agents to dynamically transport your stream data and logs from your systems into Hadoop.
A step-by-step book that guides you through the architecture and components of Flume covering different approaches, which are then pulled together as a real-world, end-to-end use case, gradually going from the simplest to the most advanced features.
