Work with pipeline logs (original) (raw)

You can use the Apache Beam SDK's built-in logging infrastructure to log information when running your pipeline. You can use theGoogle Cloud console to monitor logging information during and after your pipeline runs.

Add log messages to your pipeline

Java

The Apache Beam SDK for Java recommends that you log worker messages through the open source Simple Logging Facade for Java (SLF4J) library. The Apache Beam SDK for Java implements the required logging infrastructure so that your Java code only needs to import the SLF4J API. Then, it instantiates a Logger to enable message logging within your pipeline code.

For pre-existing code and/or libraries, the Apache Beam SDK for Java sets up additional logging infrastructure. Log messages produced by the following logging libraries for Java are captured:

Python

The Apache Beam SDK for Python provides the logging library package, which allows the pipeline workers to output log messages. To use the library functions, you must import the library:

import logging

Go

The Apache Beam SDK for Go provides the log library package, which allows the pipeline workers to output log messages. To use the library functions, you must import the library:

import "github.com/apache/beam/sdks/v2/go/pkg/beam/log"

Worker log message code example

Java

The following example uses SLF4J for Dataflow logging. To learn more about configuring SLF4J for Dataflow logging, see the Java Tips article.

The Apache BeamWordCount example can be modified to output a log message when the word "love" is found in a line of the processed text. The added code is indicated in bold in the following example (surrounding code is included for context).

package org.apache.beam.examples; // Import SLF4J packages. import org.slf4j.Logger; import org.slf4j.LoggerFactory; ... public class WordCount { ... static class ExtractWordsFn extends DoFn<String, String> { // Instantiate Logger. // Suggestion: As shown, specify the class name of the containing class // (WordCount). private static final Logger LOG = LoggerFactory.getLogger(WordCount.class); ... @ProcessElement public void processElement(ProcessContext c) { ... // Output each word encountered into the output PCollection. for (String word : words) { if (!word.isEmpty()) { c.output(word); } // Log INFO messages when the word "love" is found. if(word.toLowerCase().equals("love")) { LOG.info("Found " + word.toLowerCase()); } } } } ... // Remaining WordCount example code ...

Python

The Apache Beamwordcount.pyexample can be modified to output a log message when the word "love" is found in a line of the processed text.

import Python logging module.

import logging

class ExtractWordsFn(beam.DoFn): def process(self, element): words = re.findall(r'[A-Za-z']+', element) for word in words: yield word

  if word.lower() == 'love':
    # Log using the root logger at info or higher levels
    logging.info('Found : %s', word.lower())

Remaining WordCount example code ...

Go

The Apache Beamwordcount.goexample can be modified to output a log message when the word "love" is found in a line of the processed text.

func (f *extractFn) ProcessElement(ctx context.Context, line string, emit func(string)) { for _, word := range wordRE.FindAllString(line, -1) { // increment the counter for small words if length of words is // less than small_word_length if strings.ToLower(word) == "love" { log.Infof(ctx, "Found : %s", strings.ToLower(word)) }

    emit(word)
}

}

// Remaining Wordcount example

Java

If the modified WordCount pipeline is run locally using the default DirectRunner with the output sent to a local file (--output=./local-wordcounts), console output includes the added log messages:

INFO: Executing pipeline using the DirectRunner. ... Feb 11, 2015 1:13:22 PM org.apache.beam.examples.WordCount$ExtractWordsFn processElement INFO: Found love Feb 11, 2015 1:13:22 PM org.apache.beam.examples.WordCount$ExtractWordsFn processElement INFO: Found love Feb 11, 2015 1:13:22 PM org.apache.beam.examples.WordCount$ExtractWordsFn processElement INFO: Found love ... INFO: Pipeline execution complete.

By default, only log lines marked INFO and higher are sent to Cloud Logging. To change this behavior, seeSetting Pipeline Worker Log Levels.

Python

If the modified WordCount pipeline is run locally using the default DirectRunner with the output sent to a local file (--output=./local-wordcounts), console output includes the added log messages:

INFO:root:Found : love INFO:root:Found : love INFO:root:Found : love

By default, only log lines marked INFO and higher are sent to Cloud Logging. To change this behavior, seeSetting Pipeline Worker Log Levels.

Don't overwrite the logging configuration with logging.config functions, as this might disable the pre-configured log handlers that transmit the pipeline logs to Dataflow and Cloud Logging.

Go

If the modified WordCount pipeline is run locally using the default DirectRunner with the output sent to a local file (--output=./local-wordcounts), console output includes the added log messages:

2022/05/26 11:36:44 Found : love 2022/05/26 11:36:44 Found : love 2022/05/26 11:36:44 Found : love

By default, only log lines marked INFO and higher are sent to Cloud Logging.

Control log volume

You might also reduce the volume of logs generated by changing the pipeline log levels. If you don't want to continue ingesting some or all of your Dataflow logs, add a Logging exclusion to exclude Dataflow logs. Then, export the logs to a different destination such as BigQuery, Cloud Storage, or Pub/Sub. For more information, seeControl Dataflow log ingestion.

Logging limit and throttling

Worker log messages are limited to 15,000 messages every 30 seconds, per worker. If this limit is reached, a single worker log message is added saying that logging is throttled:

Throttling logger worker. It used up its 30s quota for logs in only 12.345s

No more messages are logged until the 30 second-interval is over. This limit is shared by log messages generated by the Apache Beam SDK and user code.

Log storage and retention

Operational logs are stored in the_Default log bucket. The logging API service name is dataflow.googleapis.com. For more information about the Google Cloud monitored resource types and services used in Cloud Logging, see Monitored resources and services.

For details about how long log entries are retained by Logging, see the retention information in Quotas and limits: Logs retention periods.

For information about viewing operational logs, see Monitor and view pipeline logs.

Monitor and view pipeline logs

When you run your pipeline on the Dataflow service, you can use the Dataflow monitoring interface to view logs emitted by your pipeline.

Dataflow worker log example

The modified WordCount pipeline can be run in the cloud with the following options:

Java

--project=WordCountExample --output=gs:///counts --runner=DataflowRunner --tempLocation=gs:///temp --stagingLocation=gs:///binaries

Python

--project=WordCountExample --output=gs:///counts --runner=DataflowRunner --staging_location=gs:///binaries

Go

--project=WordCountExample --output=gs:///counts --runner=DataflowRunner --staging_location=gs:///binaries

View logs

Because the WordCount cloud pipeline uses blocking execution, console messages are output during pipeline execution. After the job starts, a link to the Google Cloud console page is output to the console, followed by the pipeline job ID:

INFO: To access the Dataflow monitoring console, please navigate to https://console.developers.google.com/dataflow/job/2017-04-13_13_58_10-6217777367720337669 Submitted job: 2017-04-13_13_58_10-6217777367720337669

The console URL leads to the Dataflowmonitoring interface with a summary page for the submitted job. It shows a dynamic execution graph on the left, with summary information on the right. Click on the bottom panel to expand the logs panel.

The logs panel defaults to showing Job Logs that report the status of the job as a whole. You can filter the messages that appear in the logs panel by clicking Infoand Filter logs.

Selecting a pipeline step in the graph changes the view to Step Logsgenerated by your code and the generated code running in the pipeline step.

To get back to Job Logs, clear the step by clicking outside the graph or using the Deselect step button in the right side panel.

To open Logs Explorer and select different log types, in the logs panel, click View in Logs Explorer (the external link button).

In Logs Explorer, to see the panel with different log types, click the Log fields toggle.

On the Logs Explorer page, the query might filter the logs by job step or by log type. To remove filters, click the Show query toggle and edit the query.

To see all logs available for a job, follow these steps:

  1. In the Query field, enter the following query:
resource.type="dataflow_step"  
resource.labels.job_id="JOB_ID"

Replace JOB_ID with the ID of your job. 2. Click Run query. 3. If you use this query and don't see logs for your job, click Edit time. 4. Adjust the start time and end time, and then click Apply.

Log types

Logs Explorer also includes infrastructure logs for your pipeline. Use error and warning logs to diagnose observed pipeline issues. Errors and warnings in the infrastructure logs that aren't correlated with a pipeline issue don't necessarily indicate a problem.

Here's a summary of the different log types available for viewing from theLogs Explorer page:

Set pipeline worker log levels

Java

The default SLF4J logging level set on workers by the Apache Beam SDK for Java isINFO. All log messages of INFO or higher (INFO,WARN, ERROR) will be emitted. You can set a different default log level to support lower SLF4J logging levels (TRACE or DEBUG) or set different log levels for different packages of classes in your code.

The following pipeline options are provided to let you set worker log levels from the command line or programmatically:

The following example programmatically sets pipeline logging options with default values that can be overridden from the command line:

PipelineOptions options = ... SdkHarnessOptions loggingOptions = options.as(SdkHarnessOptions.class); // Overrides the default log level on the worker to emit logs at TRACE or higher. loggingOptions.setDefaultSdkHarnessLogLevel(LogLevel.TRACE); // Overrides the Foo class and "org.apache.beam.runners.dataflow" package to emit logs at WARN or higher. loggingOptions.getSdkHarnessLogLevelOverrides() .addOverrideForClass(Foo.class, LogLevel.WARN) .addOverrideForPackage(Package.getPackage("org.apache.beam.runners.dataflow"), LogLevel.WARN);

Python

The default logging level set on workers by the Apache Beam SDK for Python isINFO. All log messages of INFO or higher (INFO,WARNING, ERROR, CRITICAL) will be emitted. You can set a different default log level to support lower logging levels (DEBUG) or set different log levels for different modules in your code.

Two pipeline options are provided to let you set worker log levels from the command line or programmatically:

The following example uses theWorkerOptionsclass to programmatically set pipeline logging options that can be overridden from the command line:

from apache_beam.options.pipeline_options import PipelineOptions, WorkerOptions

pipeline_args = [ '--project=PROJECT_NAME', '--job_name=JOB_NAME', '--staging_location=gs://STORAGE_BUCKET/staging/', '--temp_location=gs://STORAGE_BUCKET/tmp/', '--region=DATAFLOW_REGION', '--runner=DataflowRunner' ]

pipeline_options = PipelineOptions(pipeline_args) worker_options = pipeline_options.view_as(WorkerOptions) worker_options.default_sdk_harness_log_level = 'WARNING'

Note: In Apache Beam SDK 2.42.0 and earlier versions, use ['{"apache_beam.runners.dataflow":"WARNING"}']

worker_options.sdk_harness_log_level_overrides = {"apache_beam.runners.dataflow":"WARNING"}

Pass in pipeline options during pipeline creation.

with beam.Pipeline(options=pipeline_options) as pipeline:

Replace the following:

Go

This feature is not available in the Apache Beam SDK for Go.

View the log of launched BigQuery jobs

When using BigQuery in your Dataflow pipeline, BigQuery jobs are launched to perform various actions on your behalf. These actions might include loading data, exporting data, and other similar tasks. For troubleshooting and monitoring purposes, the Dataflow monitoring interface has additional information on these BigQuery jobs available in the Logs panel.

The BigQuery jobs information displayed in the Logs panel is stored and loaded from a BigQuery system table. A billing cost is incurred when the underlying BigQuery table is queried.

View the BigQuery job details

To view the BigQuery jobs information, your pipeline must use Apache Beam 2.24.0 or later.

To list the BigQuery jobs, open the BigQuery Jobs tab and select the location of the BigQuery jobs. Next, click Load BigQuery Jobs and confirm the dialog. After the query completes, the jobs list is displayed.

The Load BigQuery Jobs button in the BigQuery jobs information table

Basic information about each job is provided including job ID, type, duration, and other details.

A table showing the BigQuery jobs that were run during the current pipeline job execution.

For more detailed information on a specific job, click Command line in theMore Info column.

In the modal window for the command line, copy the bq jobs describe command and run it locally or in Cloud Shell.

gcloud alpha bq jobs describe BIGQUERY_JOB_ID

The bq jobs describe command outputsJobStatistics, which provide further details that are useful when diagnosing a slow or stuck BigQuery job.

Alternatively, when you useBigQueryIOwith a SQL query, a query job is issued. To see the SQL query used by the job, click View query in the More Info column.

View diagnostics

The Diagnostics tab of the Logs pane collects and displays certain log entries produced in your pipelines. These entries include messages that indicate a probable issue with the pipeline and error messages with stack traces. Collected log entries are deduplicated and combined into error groups.

The Diagnostics tab for a Dataflow job with a Service Error error group.

The error report includes the following information:

To view the error report for a specific error, click the description under the Errors column. The Error reporting page is displayed. If the error is a Service Error, a Troubleshooting guide link displays.

The error group detail page for a Dataflow Service Error.

To know more about the page, see View and filter errors.

Mute an error

To mute an error message, follow these steps:

  1. Open the Diagnostics tab.
  2. Click the error that you want to mute.
  3. Open the resolution status menu. The statuses have the following labels:Open, Acknowledged, Resolved, or Muted.
  4. Select Muted.