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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2021/02/16 15:52:46 UTC
[GitHub] [beam] KhaninArtur opened a new pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
KhaninArtur opened a new pull request #13995:
URL: https://github.com/apache/beam/pull/13995
Some users may want to protect their sensitive data using tokenization.
We propose to create a Beam example template that will demonstrate Beam transform to protect sensitive data using tokenization. In our example, we use an external service for the data tokenization.
At a high level, a pipeline that:
- supports batch (GCS) and streaming (Pub/Sub) input sources
- tokenizes sensitive data via external REST service - we are about to use Protegrity
- outputs tokenized data into BigQuery or BigTable
------------------------
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[GitHub] [beam] pabloem commented on pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
pabloem commented on pull request #13995:
URL: https://github.com/apache/beam/pull/13995#issuecomment-786266997
hi @KhaninArtur ! thanks for your contribution - is this PR ready to be reviewed?
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[GitHub] [beam] KhaninArtur commented on pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
KhaninArtur commented on pull request #13995:
URL: https://github.com/apache/beam/pull/13995#issuecomment-792804891
Hi, @pabloem!
Are there any updates regarding this PR?
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[GitHub] [beam] KhaninArtur commented on a change in pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
KhaninArtur commented on a change in pull request #13995:
URL: https://github.com/apache/beam/pull/13995#discussion_r598693312
##########
File path: examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/transforms/DataProtectors.java
##########
@@ -0,0 +1,327 @@
+/*
+ * 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.
+ */
+package org.apache.beam.examples.complete.datatokenization.transforms;
+
+import static org.apache.beam.sdk.util.RowJsonUtils.rowToJson;
+import static org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.MoreObjects.firstNonNull;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.auto.value.AutoValue;
+import java.io.IOException;
+import java.nio.charset.Charset;
+import java.nio.charset.StandardCharsets;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.UUID;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElement;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElementCoder;
+import org.apache.beam.sdk.coders.RowCoder;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.Schema.Field;
+import org.apache.beam.sdk.schemas.Schema.FieldType;
+import org.apache.beam.sdk.state.BagState;
+import org.apache.beam.sdk.state.StateSpec;
+import org.apache.beam.sdk.state.StateSpecs;
+import org.apache.beam.sdk.state.TimeDomain;
+import org.apache.beam.sdk.state.Timer;
+import org.apache.beam.sdk.state.TimerSpec;
+import org.apache.beam.sdk.state.TimerSpecs;
+import org.apache.beam.sdk.state.ValueState;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.RowJson;
+import org.apache.beam.sdk.util.RowJsonUtils;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollectionTuple;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.sdk.values.TupleTagList;
+import org.apache.beam.vendor.grpc.v1p26p0.com.google.gson.Gson;
+import org.apache.beam.vendor.grpc.v1p26p0.com.google.gson.JsonArray;
+import org.apache.beam.vendor.grpc.v1p26p0.com.google.gson.JsonObject;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Throwables;
+import org.apache.commons.io.IOUtils;
+import org.apache.http.HttpEntity;
+import org.apache.http.client.methods.CloseableHttpResponse;
+import org.apache.http.client.methods.HttpPost;
+import org.apache.http.entity.ByteArrayEntity;
+import org.apache.http.entity.ContentType;
+import org.apache.http.impl.client.CloseableHttpClient;
+import org.apache.http.impl.client.HttpClients;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * The {@link DataProtectors} Using passing parameters transform will buffer input rows in batch and
+ * will send it when the count of buffered rows will equal specified batch size. When it takes the
+ * last one batch, it will send it when the last row will come to doFn even count of buffered rows
+ * will less than the batch size.
+ */
+public class DataProtectors {
+
+ /** Logger for class. */
+ private static final Logger LOG = LoggerFactory.getLogger(DataProtectors.class);
+
+ public static final String ID_FIELD_NAME = "ID";
+
+ /**
+ * The {@link RowToTokenizedRow} transform converts {@link Row} to {@link TableRow} objects. The
+ * transform accepts a {@link FailsafeElement} object so the original payload of the incoming
+ * record can be maintained across multiple series of transforms.
+ */
+ @AutoValue
+ public abstract static class RowToTokenizedRow<T>
+ extends PTransform<PCollection<KV<Integer, Row>>, PCollectionTuple> {
+
+ public static <T> Builder<T> newBuilder() {
+ return new AutoValue_DataProtectors_RowToTokenizedRow.Builder<>();
+ }
+
+ public abstract TupleTag<Row> successTag();
+
+ public abstract TupleTag<FailsafeElement<Row, Row>> failureTag();
+
+ public abstract Schema schema();
+
+ public abstract int batchSize();
+
+ public abstract String rpcURI();
+
+ @Override
+ public PCollectionTuple expand(PCollection<KV<Integer, Row>> inputRows) {
+ FailsafeElementCoder<Row, Row> coder =
+ FailsafeElementCoder.of(RowCoder.of(schema()), RowCoder.of(schema()));
+ PCollectionTuple pCollectionTuple =
+ inputRows.apply(
+ "Tokenize",
+ ParDo.of(new TokenizationFn(schema(), batchSize(), rpcURI(), failureTag()))
+ .withOutputTags(successTag(), TupleTagList.of(failureTag())));
+ return PCollectionTuple.of(
+ successTag(), pCollectionTuple.get(successTag()).setRowSchema(schema()))
+ .and(failureTag(), pCollectionTuple.get(failureTag()).setCoder(coder));
+ }
+
+ /** Builder for {@link RowToTokenizedRow}. */
+ @AutoValue.Builder
+ public abstract static class Builder<T> {
+
+ public abstract Builder<T> setSuccessTag(TupleTag<Row> successTag);
+
+ public abstract Builder<T> setFailureTag(TupleTag<FailsafeElement<Row, Row>> failureTag);
+
+ public abstract Builder<T> setSchema(Schema schema);
+
+ public abstract Builder<T> setBatchSize(int batchSize);
+
+ public abstract Builder<T> setRpcURI(String rpcURI);
+
+ public abstract RowToTokenizedRow<T> build();
+ }
+ }
+
+ /** Class implements stateful doFn for data tokenization using remote RPC. */
+ @SuppressWarnings("initialization.static.fields.uninitialized")
+ public static class TokenizationFn extends DoFn<KV<Integer, Row>, Row> {
+
+ private static Schema schemaToRpc;
+ private static CloseableHttpClient httpclient;
+ private static ObjectMapper objectMapperSerializerForSchema;
+ private static ObjectMapper objectMapperDeserializerForSchema;
+
+ private final Schema schema;
+ private final int batchSize;
+ private final String rpcURI;
+ private final TupleTag<FailsafeElement<Row, Row>> failureTag;
+
+ @StateId("buffer")
+ private final StateSpec<BagState<Row>> bufferedEvents;
+
+ @StateId("count")
+ private final StateSpec<ValueState<Integer>> countState = StateSpecs.value();
+
+ @TimerId("expiry")
+ private final TimerSpec expirySpec = TimerSpecs.timer(TimeDomain.EVENT_TIME);
+
+ private Map<String, Row> inputRowsWithIds;
+
+ public TokenizationFn(
+ Schema schema,
+ int batchSize,
+ String rpcURI,
+ TupleTag<FailsafeElement<Row, Row>> failureTag) {
+ this.schema = schema;
+ this.batchSize = batchSize;
+ this.rpcURI = rpcURI;
+ bufferedEvents = StateSpecs.bag(RowCoder.of(schema));
+ this.failureTag = failureTag;
+ this.inputRowsWithIds = new HashMap<>();
+ }
+
+ @Setup
+ public void setup() {
+
+ List<Field> fields = schema.getFields();
+ fields.add(Field.of(ID_FIELD_NAME, FieldType.STRING));
+ schemaToRpc = new Schema(fields);
+
+ objectMapperSerializerForSchema =
+ RowJsonUtils.newObjectMapperWith(RowJson.RowJsonSerializer.forSchema(schemaToRpc));
+
+ objectMapperDeserializerForSchema =
+ RowJsonUtils.newObjectMapperWith(RowJson.RowJsonDeserializer.forSchema(schemaToRpc));
+
+ httpclient = HttpClients.createDefault();
+ }
+
+ @Teardown
+ public void close() {
+ try {
+ httpclient.close();
+ } catch (IOException exception) {
+ String exceptionMessage = exception.getMessage();
+ if (exceptionMessage != null) {
+ LOG.warn("Can't close connection: {}", exceptionMessage);
+ }
+ }
+ }
+
+ @OnTimer("expiry")
+ public void onExpiry(OnTimerContext context, @StateId("buffer") BagState<Row> bufferState) {
+ boolean isEmpty = firstNonNull(bufferState.isEmpty().read(), true);
+ if (!isEmpty) {
+ processBufferedRows(bufferState.read(), context);
+ bufferState.clear();
+ }
+ }
+
+ @ProcessElement
+ public void process(
+ ProcessContext context,
+ BoundedWindow window,
+ @StateId("buffer") BagState<Row> bufferState,
+ @StateId("count") ValueState<Integer> countState,
+ @TimerId("expiry") Timer expiryTimer) {
+
+ expiryTimer.set(window.maxTimestamp());
+
+ int count = firstNonNull(countState.read(), 0);
+ count++;
+ countState.write(count);
+ bufferState.add(context.element().getValue());
+
+ if (count >= batchSize) {
+ processBufferedRows(bufferState.read(), context);
+ bufferState.clear();
+ countState.clear();
+ }
+ }
+
+ @SuppressWarnings("argument.type.incompatible")
+ private void processBufferedRows(Iterable<Row> rows, WindowedContext context) {
Review comment:
Thank you so much for your suggestion, @pabloem! GroupIntoBatches works really great, replaced Stateful DoFn with it.
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[GitHub] [beam] KhaninArtur commented on a change in pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
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.
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[GitHub] [beam] pabloem commented on a change in pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
pabloem commented on a change in pull request #13995:
URL: https://github.com/apache/beam/pull/13995#discussion_r589778288
##########
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>"
+```
+
Review comment:
these instructions are nice and useful. I worry that users will not find out about this example. Do you have plans to blog about it, or add any extra documentation for it?
##########
File path: examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/transforms/DataProtectors.java
##########
@@ -0,0 +1,327 @@
+/*
+ * 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.
+ */
+package org.apache.beam.examples.complete.datatokenization.transforms;
+
+import static org.apache.beam.sdk.util.RowJsonUtils.rowToJson;
+import static org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.MoreObjects.firstNonNull;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.auto.value.AutoValue;
+import java.io.IOException;
+import java.nio.charset.Charset;
+import java.nio.charset.StandardCharsets;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.UUID;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElement;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElementCoder;
+import org.apache.beam.sdk.coders.RowCoder;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.Schema.Field;
+import org.apache.beam.sdk.schemas.Schema.FieldType;
+import org.apache.beam.sdk.state.BagState;
+import org.apache.beam.sdk.state.StateSpec;
+import org.apache.beam.sdk.state.StateSpecs;
+import org.apache.beam.sdk.state.TimeDomain;
+import org.apache.beam.sdk.state.Timer;
+import org.apache.beam.sdk.state.TimerSpec;
+import org.apache.beam.sdk.state.TimerSpecs;
+import org.apache.beam.sdk.state.ValueState;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.util.RowJson;
+import org.apache.beam.sdk.util.RowJsonUtils;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollectionTuple;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.sdk.values.TupleTagList;
+import org.apache.beam.vendor.grpc.v1p26p0.com.google.gson.Gson;
+import org.apache.beam.vendor.grpc.v1p26p0.com.google.gson.JsonArray;
+import org.apache.beam.vendor.grpc.v1p26p0.com.google.gson.JsonObject;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Throwables;
+import org.apache.commons.io.IOUtils;
+import org.apache.http.HttpEntity;
+import org.apache.http.client.methods.CloseableHttpResponse;
+import org.apache.http.client.methods.HttpPost;
+import org.apache.http.entity.ByteArrayEntity;
+import org.apache.http.entity.ContentType;
+import org.apache.http.impl.client.CloseableHttpClient;
+import org.apache.http.impl.client.HttpClients;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * The {@link DataProtectors} Using passing parameters transform will buffer input rows in batch and
+ * will send it when the count of buffered rows will equal specified batch size. When it takes the
+ * last one batch, it will send it when the last row will come to doFn even count of buffered rows
+ * will less than the batch size.
+ */
+public class DataProtectors {
+
+ /** Logger for class. */
+ private static final Logger LOG = LoggerFactory.getLogger(DataProtectors.class);
+
+ public static final String ID_FIELD_NAME = "ID";
+
+ /**
+ * The {@link RowToTokenizedRow} transform converts {@link Row} to {@link TableRow} objects. The
+ * transform accepts a {@link FailsafeElement} object so the original payload of the incoming
+ * record can be maintained across multiple series of transforms.
+ */
+ @AutoValue
+ public abstract static class RowToTokenizedRow<T>
+ extends PTransform<PCollection<KV<Integer, Row>>, PCollectionTuple> {
+
+ public static <T> Builder<T> newBuilder() {
+ return new AutoValue_DataProtectors_RowToTokenizedRow.Builder<>();
+ }
+
+ public abstract TupleTag<Row> successTag();
+
+ public abstract TupleTag<FailsafeElement<Row, Row>> failureTag();
+
+ public abstract Schema schema();
+
+ public abstract int batchSize();
+
+ public abstract String rpcURI();
+
+ @Override
+ public PCollectionTuple expand(PCollection<KV<Integer, Row>> inputRows) {
+ FailsafeElementCoder<Row, Row> coder =
+ FailsafeElementCoder.of(RowCoder.of(schema()), RowCoder.of(schema()));
+ PCollectionTuple pCollectionTuple =
+ inputRows.apply(
+ "Tokenize",
+ ParDo.of(new TokenizationFn(schema(), batchSize(), rpcURI(), failureTag()))
+ .withOutputTags(successTag(), TupleTagList.of(failureTag())));
+ return PCollectionTuple.of(
+ successTag(), pCollectionTuple.get(successTag()).setRowSchema(schema()))
+ .and(failureTag(), pCollectionTuple.get(failureTag()).setCoder(coder));
+ }
+
+ /** Builder for {@link RowToTokenizedRow}. */
+ @AutoValue.Builder
+ public abstract static class Builder<T> {
+
+ public abstract Builder<T> setSuccessTag(TupleTag<Row> successTag);
+
+ public abstract Builder<T> setFailureTag(TupleTag<FailsafeElement<Row, Row>> failureTag);
+
+ public abstract Builder<T> setSchema(Schema schema);
+
+ public abstract Builder<T> setBatchSize(int batchSize);
+
+ public abstract Builder<T> setRpcURI(String rpcURI);
+
+ public abstract RowToTokenizedRow<T> build();
+ }
+ }
+
+ /** Class implements stateful doFn for data tokenization using remote RPC. */
+ @SuppressWarnings("initialization.static.fields.uninitialized")
+ public static class TokenizationFn extends DoFn<KV<Integer, Row>, Row> {
+
+ private static Schema schemaToRpc;
+ private static CloseableHttpClient httpclient;
+ private static ObjectMapper objectMapperSerializerForSchema;
+ private static ObjectMapper objectMapperDeserializerForSchema;
+
+ private final Schema schema;
+ private final int batchSize;
+ private final String rpcURI;
+ private final TupleTag<FailsafeElement<Row, Row>> failureTag;
+
+ @StateId("buffer")
+ private final StateSpec<BagState<Row>> bufferedEvents;
+
+ @StateId("count")
+ private final StateSpec<ValueState<Integer>> countState = StateSpecs.value();
+
+ @TimerId("expiry")
+ private final TimerSpec expirySpec = TimerSpecs.timer(TimeDomain.EVENT_TIME);
+
+ private Map<String, Row> inputRowsWithIds;
+
+ public TokenizationFn(
+ Schema schema,
+ int batchSize,
+ String rpcURI,
+ TupleTag<FailsafeElement<Row, Row>> failureTag) {
+ this.schema = schema;
+ this.batchSize = batchSize;
+ this.rpcURI = rpcURI;
+ bufferedEvents = StateSpecs.bag(RowCoder.of(schema));
+ this.failureTag = failureTag;
+ this.inputRowsWithIds = new HashMap<>();
+ }
+
+ @Setup
+ public void setup() {
+
+ List<Field> fields = schema.getFields();
+ fields.add(Field.of(ID_FIELD_NAME, FieldType.STRING));
+ schemaToRpc = new Schema(fields);
+
+ objectMapperSerializerForSchema =
+ RowJsonUtils.newObjectMapperWith(RowJson.RowJsonSerializer.forSchema(schemaToRpc));
+
+ objectMapperDeserializerForSchema =
+ RowJsonUtils.newObjectMapperWith(RowJson.RowJsonDeserializer.forSchema(schemaToRpc));
+
+ httpclient = HttpClients.createDefault();
+ }
+
+ @Teardown
+ public void close() {
+ try {
+ httpclient.close();
+ } catch (IOException exception) {
+ String exceptionMessage = exception.getMessage();
+ if (exceptionMessage != null) {
+ LOG.warn("Can't close connection: {}", exceptionMessage);
+ }
+ }
+ }
+
+ @OnTimer("expiry")
+ public void onExpiry(OnTimerContext context, @StateId("buffer") BagState<Row> bufferState) {
+ boolean isEmpty = firstNonNull(bufferState.isEmpty().read(), true);
+ if (!isEmpty) {
+ processBufferedRows(bufferState.read(), context);
+ bufferState.clear();
+ }
+ }
+
+ @ProcessElement
+ public void process(
+ ProcessContext context,
+ BoundedWindow window,
+ @StateId("buffer") BagState<Row> bufferState,
+ @StateId("count") ValueState<Integer> countState,
+ @TimerId("expiry") Timer expiryTimer) {
+
+ expiryTimer.set(window.maxTimestamp());
+
+ int count = firstNonNull(countState.read(), 0);
+ count++;
+ countState.write(count);
+ bufferState.add(context.element().getValue());
+
+ if (count >= batchSize) {
+ processBufferedRows(bufferState.read(), context);
+ bufferState.clear();
+ countState.clear();
+ }
+ }
+
+ @SuppressWarnings("argument.type.incompatible")
+ private void processBufferedRows(Iterable<Row> rows, WindowedContext context) {
Review comment:
I see that this DoFn does a lot of its own buffering. Have you considered using GroupIntoBatches[1] for this? GroupIntoBatches has the same sort of buffering/counting/timer emission logic, but it receives more updates (e.g. it recently started supporting 'autosharding' which lets runners decouple the number of shards from the number of keys).
Think about it - but I recommend you try using GroupIntoBatches, as you will get some extra nice benefits from it.
[1] https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/transforms/GroupIntoBatches.html
##########
File path: examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/transforms/io/FileSystemIO.java
##########
@@ -0,0 +1,248 @@
+/*
+ * 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.
+ */
+package org.apache.beam.examples.complete.datatokenization.transforms.io;
+
+import org.apache.beam.examples.complete.datatokenization.options.DataTokenizationOptions;
+import org.apache.beam.examples.complete.datatokenization.utils.CsvConverters;
+import org.apache.beam.examples.complete.datatokenization.utils.ErrorConverters;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElement;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElementCoder;
+import org.apache.beam.examples.complete.datatokenization.utils.RowToCsv;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.NullableCoder;
+import org.apache.beam.sdk.coders.StringUtf8Coder;
+import org.apache.beam.sdk.io.TextIO;
+import org.apache.beam.sdk.options.Default;
+import org.apache.beam.sdk.options.Description;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.transforms.MapElements;
+import org.apache.beam.sdk.transforms.ToJson;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollection.IsBounded;
+import org.apache.beam.sdk.values.PCollectionTuple;
+import org.apache.beam.sdk.values.PDone;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.sdk.values.TypeDescriptors;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/** The {@link FileSystemIO} class to read/write data from/into File Systems. */
+public class FileSystemIO {
Review comment:
is this class meant to be generic? Or specific for this template? I see the class is within the template package - I am just wondering if we should name the class TokenizationFileIO or something that states clearly that these transforms are meant only to be used for the data tokenization template?
##########
File path: examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/transforms/io/BigQueryIO.java
##########
@@ -0,0 +1,98 @@
+/*
+ * 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.
+ */
+package org.apache.beam.examples.complete.datatokenization.transforms.io;
+
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.api.services.bigquery.model.TableSchema;
+import java.io.IOException;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElement;
+import org.apache.beam.sdk.io.gcp.bigquery.BigQueryInsertError;
+import org.apache.beam.sdk.io.gcp.bigquery.BigQueryUtils;
+import org.apache.beam.sdk.io.gcp.bigquery.InsertRetryPolicy;
+import org.apache.beam.sdk.io.gcp.bigquery.WriteResult;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.Row;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/** The {@link BigQueryIO} class for writing data from template to BigTable. */
+public class BigQueryIO {
Review comment:
Same as FileSystemIO
##########
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:
I don't see anything in this address. Will it be added later?
##########
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:
I am curious how the template here and in DataflowTemplates will be different?
##########
File path: examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/transforms/io/BigTableIO.java
##########
@@ -0,0 +1,158 @@
+/*
+ * 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.
+ */
+package org.apache.beam.examples.complete.datatokenization.transforms.io;
+
+import com.google.bigtable.v2.Mutation;
+import com.google.protobuf.ByteString;
+import java.nio.charset.StandardCharsets;
+import java.util.Objects;
+import java.util.Set;
+import java.util.stream.Collectors;
+import org.apache.beam.examples.complete.datatokenization.options.DataTokenizationOptions;
+import org.apache.beam.sdk.io.gcp.bigtable.BigtableIO;
+import org.apache.beam.sdk.io.gcp.bigtable.BigtableWriteResult;
+import org.apache.beam.sdk.options.Description;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PDone;
+import org.apache.beam.sdk.values.Row;
+import org.apache.commons.lang3.tuple.Pair;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/** The {@link BigTableIO} class for writing data from template to BigTable. */
+public class BigTableIO {
Review comment:
Same question as with FileSystemIO
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[GitHub] [beam] KhaninArtur commented on pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
KhaninArtur commented on pull request #13995:
URL: https://github.com/apache/beam/pull/13995#issuecomment-786488421
Hi @pabloem! Yes, this PR is ready to be reviewed 👍🏼
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[GitHub] [beam] pabloem merged pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
pabloem merged pull request #13995:
URL: https://github.com/apache/beam/pull/13995
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[GitHub] [beam] KhaninArtur commented on a change in pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
KhaninArtur commented on a change in pull request #13995:
URL: https://github.com/apache/beam/pull/13995#discussion_r598691216
##########
File path: examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/transforms/io/FileSystemIO.java
##########
@@ -0,0 +1,248 @@
+/*
+ * 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.
+ */
+package org.apache.beam.examples.complete.datatokenization.transforms.io;
+
+import org.apache.beam.examples.complete.datatokenization.options.DataTokenizationOptions;
+import org.apache.beam.examples.complete.datatokenization.utils.CsvConverters;
+import org.apache.beam.examples.complete.datatokenization.utils.ErrorConverters;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElement;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElementCoder;
+import org.apache.beam.examples.complete.datatokenization.utils.RowToCsv;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.NullableCoder;
+import org.apache.beam.sdk.coders.StringUtf8Coder;
+import org.apache.beam.sdk.io.TextIO;
+import org.apache.beam.sdk.options.Default;
+import org.apache.beam.sdk.options.Description;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.transforms.MapElements;
+import org.apache.beam.sdk.transforms.ToJson;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollection.IsBounded;
+import org.apache.beam.sdk.values.PCollectionTuple;
+import org.apache.beam.sdk.values.PDone;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.sdk.values.TypeDescriptors;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/** The {@link FileSystemIO} class to read/write data from/into File Systems. */
+public class FileSystemIO {
Review comment:
Got you, renamed it.
##########
File path: examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/transforms/io/BigTableIO.java
##########
@@ -0,0 +1,158 @@
+/*
+ * 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.
+ */
+package org.apache.beam.examples.complete.datatokenization.transforms.io;
+
+import com.google.bigtable.v2.Mutation;
+import com.google.protobuf.ByteString;
+import java.nio.charset.StandardCharsets;
+import java.util.Objects;
+import java.util.Set;
+import java.util.stream.Collectors;
+import org.apache.beam.examples.complete.datatokenization.options.DataTokenizationOptions;
+import org.apache.beam.sdk.io.gcp.bigtable.BigtableIO;
+import org.apache.beam.sdk.io.gcp.bigtable.BigtableWriteResult;
+import org.apache.beam.sdk.options.Description;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PDone;
+import org.apache.beam.sdk.values.Row;
+import org.apache.commons.lang3.tuple.Pair;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/** The {@link BigTableIO} class for writing data from template to BigTable. */
+public class BigTableIO {
Review comment:
Done
##########
File path: examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/transforms/io/BigQueryIO.java
##########
@@ -0,0 +1,98 @@
+/*
+ * 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.
+ */
+package org.apache.beam.examples.complete.datatokenization.transforms.io;
+
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.api.services.bigquery.model.TableSchema;
+import java.io.IOException;
+import org.apache.beam.examples.complete.datatokenization.utils.FailsafeElement;
+import org.apache.beam.sdk.io.gcp.bigquery.BigQueryInsertError;
+import org.apache.beam.sdk.io.gcp.bigquery.BigQueryUtils;
+import org.apache.beam.sdk.io.gcp.bigquery.InsertRetryPolicy;
+import org.apache.beam.sdk.io.gcp.bigquery.WriteResult;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.Row;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/** The {@link BigQueryIO} class for writing data from template to BigTable. */
+public class BigQueryIO {
Review comment:
Done
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[GitHub] [beam] KhaninArtur commented on pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
KhaninArtur commented on pull request #13995:
URL: https://github.com/apache/beam/pull/13995#issuecomment-803977659
retest this please
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[GitHub] [beam] KhaninArtur commented on a change in pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
KhaninArtur commented on a change in pull request #13995:
URL: https://github.com/apache/beam/pull/13995#discussion_r594966044
##########
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>"
+```
+
Review comment:
Yes, we plan to spread the word and blog about it.
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[GitHub] [beam] pabloem commented on pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
pabloem commented on pull request #13995:
URL: https://github.com/apache/beam/pull/13995#issuecomment-804241539
taking a look today
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[GitHub] [beam] KhaninArtur commented on pull request #13995: [BEAM-11322] Apache Beam Example to tokenize sensitive data
Posted by GitBox <gi...@apache.org>.
KhaninArtur commented on pull request #13995:
URL: https://github.com/apache/beam/pull/13995#issuecomment-803946707
retest this please
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