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Returns true if a value contains a string or pattern. The value to search can be a string literal, a function returning a string, or a reference to a column of String type.

Since the MATCHES function returns a Boolean value, it can be used as both a function and as a conditional.

Tip: When you select values in a histogram for a column of Array type, the function that identifies the values on which to perform a transform is typically MATCHES.

Tip: If you need the location of the matched string within the source, use the FIND function. See FIND Function.

Column reference example:

matches(ProdId, 'Fun Toy')

Output: Returns true when the value in the ProdId column value contains the string literal Fun Toy.

String literal example:

matches('Hello, World', 'Hello')

Output: Returns true.

matches(column_string,string_pattern [,ignore_case])

Argument	Required?	Data Type	Description
column_string	Y	string	Name of column or string literal to be searched
string_pattern	Y	string	Name of column, function returning a string ,or string literal or pattern to find
ignore_case	N	string	When `true`, matching is case-insensitive. Default is `false`.

column_string

Name of the column or string literal to be searched.

Missing string or column values generate missing string results.
- String constants must be quoted ('Hello, World').
Multiple columns can be specified as an array ( matches([Col1,Col2],'hello').

Required?	Data Type	Example Value
Yes	String	`MyColumn`

string_pattern

Column of strings, function returning a string, or string literal. Value can be a column reference, string literal, , or regular expression to match against the source column-string.

Required?	Data Type	Example Value
Yes	Column reference or string literal or pattern	`'home page'`

ignore_case

When true, matches are case-insensitive. Default is false.

NOTE: This argument is not required. By default, matches are case-sensitive.

Required?	Data Type	Example Value
No	String literal evaluating to a Boolean	`'true'`

Example - Filtering log data

In downloaded log files, you might see error messages of the following type:

INFO - status information on the process
WARNING - system encountered a non-fatal error during execution
ERROR - system encountered an error, which might have caused the job to fail.

For purposes of analysis, you might want to filter out the data for INFO and WARNING messages.

Source:

Here is example data from a log file of a failed job:

log
2016-01-29T00:14:24.924Z com.example.hadoopdata.monitor.spark_runner.ProfilerServiceClient [pool-13-thread-1] INFO com.example.hadoopdata.monitor.spark_runner.BatchProfileSparkRunner - Spark Profiler URL - http://localhost:4006/
2016-01-29T00:14:40.066Z com.example.hadoopdata.monitor.spark_runner.BatchProfileSparkRunner [pool-13-thread-1] INFO com.example.hadoopdata.monitor.spark_runner.BatchProfileSparkRunner - Spark process ID was null.
2016-01-29T00:14:40.067Z com.example.hadoopdata.monitor.spark_runner.BatchProfileSparkRunner [pool-13-thread-1] INFO com.example.hadoopdata.monitor.spark_runner.BatchProfileSparkRunner - --------------------------------END SPARK JOB-------------------------------
2016-01-29T00:14:44.961Z com.example.hadoopdata.joblaunch.server.BatchPollingWorker [pool-4-thread-2] ERROR com.example.hadoopdata.joblaunch.server.BatchPollingWorker - Job '128' threw an exception during execution
2016-01-29T00:14:44.962Z com.example.hadoopdata.joblaunch.server.BatchPollingWorker [pool-4-thread-2] INFO com.example.hadoopdata.joblaunch.server.BatchPollingWorker - Making sure async worker is stopped
2016-01-29T00:14:44.962Z com.example.hadoopdata.joblaunch.server.BatchPollingWorker [pool-4-thread-2] INFO com.example.hadoopdata.joblaunch.server.BatchPollingWorker - Notifying monitor for job '128', code 'FAILURE'
2016-01-29T00:14:44.988Z com.example.hadoopdata.monitor.client.MonitorClient [pool-4-thread-2] INFO com.example.hadoopdata.monitor.client.MonitorClient - Request succeeded to monitor ip-0-0-0-0.example.com:8001

Transformation:

When the above data is loaded into the application, you might want to break up the data into separate columns, which splits them on the Z character at the end of the timestamp:

Then, you can rename the two columns: Timestamp and Log_Message. To filter out the INFO and WARNING messages, you can use the following transforms, which match on the string literals to identify these messages:

Results:

After the above steps, the data should look like the following:

Timestamp	Log_Message
2016-01-29T00:14:44.961	com.example.hadoopdata.joblaunch.server.BatchPollingWorker [pool-4-thread-2] ERROR com.example.hadoopdata.joblaunch.server.BatchPollingWorker - Job '128' threw an exception during execution