[
https://issues.apache.org/jira/browse/BEAM-11322?focusedWorklogId=569685&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-569685
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ASF GitHub Bot logged work on BEAM-11322:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 22/Mar/21 13:01
Start Date: 22/Mar/21 13:01
Worklog Time Spent: 10m
Work Description: 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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Issue Time Tracking
-------------------
Worklog Id: (was: 569685)
Time Spent: 1h 50m (was: 1h 40m)
> Apache Beam Template to tokenize sensitive data
> -----------------------------------------------
>
> Key: BEAM-11322
> URL: https://issues.apache.org/jira/browse/BEAM-11322
> Project: Beam
> Issue Type: Improvement
> Components: examples-java
> Reporter: Artur Khanin
> Assignee: Artur Khanin
> Priority: P3
> Labels: Clarified
> Time Spent: 1h 50m
> Remaining Estimate: 0h
>
> 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
> will use an external service for the data tokenization.
> At a high level, a pipeline that will:
> * support batch (GCS) and streaming (Pub/Sub) input sources
> * tokenize sensitive data via external REST service - we are about to use
> Protegrity
> * output tokenized data into BigQuery or BigTable
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