MATCHES Function

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Returns true if a value contains a string or pattern. The value to search can be a string literal 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.

Basic Usage

Column reference example:

delete row: MATCHES(ProdId, 'Fun Toy')

Output: Deletes any row in which the value in the ProdId column value contains the string literal Fun Toy.

String literal example:

derive type:single value: MATCHES('Hello, World', 'Hello')

Output: For all values in the dataset returns true.

Syntax and Arguments

derive type:single value:MATCHES(column_string,string_pattern)

Argument	Required?	Data Type	Description
column_string	Y	string	Name of column or string literal to be searched
string_pattern	Y	string	String literal or pattern to find

For more information on syntax standards, see Language Documentation Syntax Notes.

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').

Usage Notes:

Required?	Data Type	Example Value
Yes	String	`MyColumn`

string_pattern

String literal, Alteryx® pattern, or regular expression to match against the source column-string.

Column references are not supported.

Usage Notes:

Required?	Data Type	Example Value
Yes	String literal or pattern	`'home page'`

Examples

Tip: For additional examples, see Common Tasks.

Example - Filtering log data

When the feature is enabled, you can download the log files for any job that fails to execute, which can assist in debugging issues related to the dataset, recipe, or job execution. In the downloaded logs, 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

Transform:

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:

split col: column1 on: `Z `

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:

delete row: MATCHES(Log_Message, '] INFO ')

delete row: MATCHES(Log_Message, '] WARNING ')

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

See Also for MATCHES Function:

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