KhaninArtur commented on a change in pull request #13995: URL: https://github.com/apache/beam/pull/13995#discussion_r594975541
########## File path: examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/README.md ########## @@ -0,0 +1,169 @@ +<!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--> + +# Apache Beam pipeline example to tokenize data using remote RPC server + +This directory contains an Apache Beam example that creates a pipeline to read data from one of +the supported sources, tokenize data with external API calls to remote RPC server, and write data into one of the supported sinks. + +Supported data formats: + +- JSON +- CSV + +Supported input sources: + +- File system +- [Google Pub/Sub](https://cloud.google.com/pubsub) + +Supported destination sinks: + +- File system +- [Google Cloud BigQuery](https://cloud.google.com/bigquery) +- [Cloud BigTable](https://cloud.google.com/bigtable) + +Supported data schema format: + +- JSON with an array of fields described in BigQuery format + +In the main scenario, the template will create an Apache Beam pipeline that will read data in CSV or +JSON format from a specified input source, send the data to an external processing server, receive +processed data, and write it into a specified output sink. + +## Requirements + +- Java 8 +- 1 of supported sources to read data from +- 1 of supported destination sinks to write data into +- A configured RPC to tokenize data + +## Getting Started + +This section describes what is needed to get the template up and running. + +- Gradle preparation +- Local execution +- Running as a Dataflow Template + - Setting Up Project Environment + - Build Data Tokenization Dataflow Flex Template + - Creating the Dataflow Flex Template + - Executing Template + +## Gradle preparation + +To run this example your `build.gradle` file should contain the following task to execute the pipeline: + +``` +task execute (type:JavaExec) { + main = System.getProperty("mainClass") + classpath = sourceSets.main.runtimeClasspath + systemProperties System.getProperties() + args System.getProperty("exec.args", "").split() +} +``` + +This task allows to run the pipeline via the following command: + +```bash +gradle clean execute -DmainClass=org.apache.beam.examples.complete.datatokenization.DataTokenization \ + -Dexec.args="--<argument>=<value> --<argument>=<value>" +``` + +## Running the pipeline + +To execute this pipeline, specify the parameters: + +- Data schema + - **dataSchemaPath**: Path to data schema (JSON format) compatible with BigQuery. +- 1 specified input source out of these: + - File System + - **inputFilePattern**: Filepattern for files to read data from + - **inputFileFormat**: File format of input files. Supported formats: JSON, CSV + - In case if input data is in CSV format: + - **csvContainsHeaders**: `true` if file(s) in bucket to read data from contain headers, + and `false` otherwise + - **csvDelimiter**: Delimiting character in CSV. Default: use delimiter provided in + csvFormat + - **csvFormat**: Csv format according to Apache Commons CSV format. Default is: + [Apache Commons CSV default](https://static.javadoc.io/org.apache.commons/commons-csv/1.7/org/apache/commons/csv/CSVFormat.html#DEFAULT) + . Must match format names exactly found + at: https://static.javadoc.io/org.apache.commons/commons-csv/1.7/org/apache/commons/csv/CSVFormat.Predefined.html + - Google Pub/Sub + - **pubsubTopic**: The Cloud Pub/Sub topic to read from, in the format of ' + projects/yourproject/topics/yourtopic' +- 1 specified output sink out of these: + - File System + - **outputDirectory**: Directory to write data to + - **outputFileFormat**: File format of output files. Supported formats: JSON, CSV + - **windowDuration**: The window duration in which data will be written. Should be specified + only for 'Pub/Sub -> GCS' case. Defaults to 30s. + + Allowed formats are: + - Ns (for seconds, example: 5s), + - Nm (for minutes, example: 12m), + - Nh (for hours, example: 2h). + - Google Cloud BigQuery + - **bigQueryTableName**: Cloud BigQuery table name to write into + - **tempLocation**: Folder in a Google Cloud Storage bucket, which is needed for + BigQuery to handle data writing + - Cloud BigTable + - **bigTableProjectId**: Id of the project where the Cloud BigTable instance to write into + is located + - **bigTableInstanceId**: Id of the Cloud BigTable instance to write into + - **bigTableTableId**: Id of the Cloud BigTable table to write into + - **bigTableKeyColumnName**: Column name to use as a key in Cloud BigTable + - **bigTableColumnFamilyName**: Column family name to use in Cloud BigTable +- RPC server parameters + - **rpcUri**: URI for the API calls to RPC server + - **batchSize**: Size of the batch to send to RPC server per request + +The template allows for the user to supply the following optional parameter: + +- **nonTokenizedDeadLetterPath**: Folder where failed to tokenize data will be stored + + +in the following format: + +```bash +--dataSchemaPath="path-to-data-schema-in-json-format" +--inputFilePattern="path-pattern-to-input-data" +--outputDirectory="path-to-output-directory" +# example for CSV case +--inputFileFormat="CSV" +--outputFileFormat="CSV" +--csvContainsHeaders="true" +--nonTokenizedDeadLetterPath="path-to-errors-rows-writing" +--batchSize=batch-size-number +--rpcUri=http://host:port/tokenize +``` + +By default, this will run the pipeline locally with the DirectRunner. To change the runner, specify: + +```bash +--runner=YOUR_SELECTED_RUNNER +``` + +See the [documentation](http://beam.apache.org/get-started/quickstart/) and +the [Examples README](../../../../../../../../../README.md) for more information about how to run this example. + +## Running as a Dataflow Template + +This example also exists as Google Dataflow Template, which you can build and run using Google Cloud Platform. See +this template documentation [README.md](https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/master/v2/protegrity-data-tokenization/README.md) for Review comment: Yes, we have [a PR](https://github.com/GoogleCloudPlatform/DataflowTemplates/pull/210) in the DataflowTemplates repository and it is focused on execution via Google Cloud Dataflow runner. The Beam version, on the other hand, is a bit more generic. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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