Flume - Architecture of Flume NG : Apache Flume. Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store. Flume is currently undergoing incubation at The Apache Software Foundation. More information on this project can be found at Flume NG is work related to new major revision of Flume and is the subject of this post.Prior to entering the incubator, Flume saw incremental releases leading up to version 0.9.4.
As Flume became adopted it became clear that certain design choices would need to be reworked in order to address problems reported in the field. Core Concepts The purpose of Flume is to provide a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store. Flow Pipeline A flow in Flume NG starts from the client. Reliability and Failure Handling. Blueprint for a Big Data Solution. In today’s world, data is money. Companies are scrambling to collect as much data as possible, in an attempt to find hidden patterns that can be acted upon to drive revenue. However, if those companies aren’t using that data, and they’re not analyzing it to find those hidden gems, the data is worthless. One of the most challenging tasks when getting started with Hadoop and building a big data solution is figuring out how to take the tools you have and put them together.
The Hadoop ecosystem encompasses about a dozen different open-source projects. Just Another Data Management System Most data management systems have a minimum of three pieces: data ingestion, data storage, and data analysis. A data ingestion system is the connection between the data source and the storage location where the data will reside, while at rest. We can take this basic architecture of ingestion, storage, and processing, and map it onto the Hadoop ecosystem, as well: Motivation: Measuring Influence results in: Analyzing Twitter Data with Apache Hadoop, Part 2: Gathering Data with Flume. This is the second article in a series about analyzing Twitter data using some of the components of the Hadoop ecosystem available in CDH, Cloudera’s open-source distribution of Apache Hadoop and related projects.
In the first article, you learned how to pull CDH components together into a single cohesive application, but to really appreciate the flexibility of each of these components, we need to dive deeper. Every story has a beginning, and every data pipeline has a source. So, to build Hadoop applications, we need to get data from a source into HDFS. Apache Flume is one way to bring data into HDFS using CDH. The Apache Flume website describes Flume as “a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.” At the most basic level, Flume enables applications to collect data from its origin and send it to a resting location, such as HDFS. Sources Sources come in two flavors: event-driven or pollable. Channels Sinks. Flume. Flume. Flume is a framework for moving chunks of data around on your network. It’s primary mission isto move log data from where it is generated (perhaps a web server) to someplace where it can actually be used – like an HDFS file system where it can be crunched by Hadoop.
Flume’s design is very flexible – the final destination for your data could also be a database like HBase or Cassandra, a search index system like Elastic Search, another file system like an S3 bucket, or any of a myriad of other configurations. Flume will also go to some efforts to make sure that your data is delivered reliably – it includes some tunable reliability features out of the box. The Flume User Guide does a good job of explaining how its component architecture works. You can configure data flows by chaining together systems of “nodes” – each node is a data moving unit – each has an input (“source”) and an output (“sink”). Flume chops the data up into a series of “events”. Enough with the intro – lets jump in. 1. Flume Architecture. Musings. 4 - Flume and Fire Fighting [ Note: This is a fairly long post. It also leans more on the technical side but I have tried to keep it as simple as possible.
I felt the need to go into this much detail because the experience described below has affected me tremendously in the way I approach work and design systems. Of the many projects I have done at Funzio this one is the most memorable and the most hard hitting. ] Around the time I joined, Funzio had decided to adopt Vertica as the analytics backend system.
It is a highly scalable data warehousing system that supports SQL. We had decided to use Flume, an open source tool which streams logs from one machine to another. The game servers log the analytics data locally to a file. Flume has a feature called end to end reliability streaming. The process of releasing our games is in two phases. During the soft launch phase Flume and Vertica held up without any hiccups, it was only few tens of thousands of records per day. Wrong. When Hadoop isn’t fast enough: The Argument for Storm | Nodeable Blog. Big Data is a Big Deal, and Hadoop is arguably the driving force in Big Data. But as awesome as Hadoop is – and it is quite awesome – it’s incomplete. For many things, Hadoop’s batch workflow is just too slow. You wouldn’t calculate trending topics for Twitter using Hadoop, nor would a hedge fund look for stock trends in real-time using Hadoop.
Because Hadoop doesn’t do real-time. So the trick is to marry the powerful batch processing capabilities of Hadoop with a front-end preprocessing engine that works in real-time. Like Storm, the project Twitter inherited when it acquired BackType in 2011. At Nodeable we use Storm to surface real-time insights from system data, whether that system is GitHub or AWS or Salesforce.com or Twitter or an infinite number of data sources. But not when you need to know something right now . There are alternatives to Storm, of course. But whether you use Storm or something else, you likely do need to figure out how to complement Hadoop with real-time.