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Chukwa aims to provide a flexible and powerful platform for distributed
data collection and rapid data processing. Our goal is to produce a system
that's usable today, but that can be modified to take advantage of newer
storage technologies (HDFS appends, HBase, etc) as they mature. In order
to maintain this flexibility, Chukwa is structured as a pipeline of
collection and processing stages, with clean and narrow interfaces between
stages. This will facilitate future innovation without breaking existing code.
Chukwa has five primary components:
* <<Adaptors>> that collect data from various data source.
* <<Agents>> that run on each machine and emit data.
* <<ETL Processes>> for parsing and archiving the data.
* <<Data Analytics Scripts>> for aggregate Hadoop cluster health.
* <<HICC>>, the Hadoop Infrastructure Care Center; a web-portal
style interface for displaying data.
Below is a figure showing the Chukwa data pipeline, annotated with data
dwell times at each stage. A more detailed figure is available at the end
of this document.
[./images/chukwa_architecture.png] Architecture
Agents and Adaptors
Chukwa agents do not collect some particular fixed set of data. Rather, they
support dynamically starting and stopping <Adaptors>, which small
dynamically-controllable modules that run inside the Agent process and are
responsible for the actual collection of data.
These dynamically controllable data sources are called
adaptors, since they generally are wrapping some other data source,
such as a file or a Unix command-line tool. The Chukwa
{{{./agent.html}agent guide}} includes an up-to-date list of available Adaptors.
Data sources need to be dynamically controllable because the particular data
being collected from a machine changes over time, and varies from machine
to machine. For example, as Hadoop tasks start and stop, different log files
must be monitored. We might want to increase our collection rate if we
detect anomalies. And of course, it makes no sense to collect Hadoop
metrics on an NFS server.
ETL Processes
Chukwa Agents can write data directly to HBase or sequence files.
This is convenient for rapidly getting data committed to stable storage.
HBase provides index by primary key, and manage data compaction. It is
better for continous monitoring of data stream, and periodically produce
HDFS provides better throughput for working with large volume of data.
It is more suitable for one time research analysis job . But it's less
convenient for finding particular data items. As a result, Chukwa has a
toolbox of MapReduce jobs for organizing and processing incoming data.
These jobs come in two kinds: <Archiving> and <Demux>.
The archiving jobs simply take Chunks from their input, and output new sequence
files of Chunks, ordered and grouped. They do no parsing or modification of
the contents. (There are several different archiving jobs, that differ in
precisely how they group the data.)
Demux, in contrast, take Chunks as input and parse them to produce
ChukwaRecords, which are sets of key-value pairs. Demux can run as a
MapReduce job or as part of HBaseWriter.
For details on controlling this part of the pipeline, see the
{{{./pipeline.html}Pipeline guide}}. For details about the file
formats, and how to use the collected data, see the {{{./programming.html}
Programming guide}}.
Data Analytics Scripts
Data stored in HBase are aggregated by data analytic scripts to
provide visualization and interpretation of health of Hadoop cluster.
Data analytics scripts are written in PigLatin, the high level language
provides easy to understand programming examples for data analyst to
create additional scripts to visualize data on HICC.
HICC, the Hadoop Infrastructure Care Center is a web-portal
style interface for displaying data. Data is fetched from HBase,
which in turn is populated by collector or data analytic scripts
that runs on the collected data, after Demux. The
{{{./admin.html}Administration guide}} has details on setting up HICC.
HBase Integration
Chukwa has adopted to use HBase to ensure data arrival in milli-seconds and
also make data available to down steam application at the same time. This
will enable monitoring application to have near realtime view as soon as
data are arriving in the system. The file rolling, archiving are replaced
by HBase Region Server minor and major compactions.