/*
* Copyright (C) 2020 Google LLC
*
* Licensed 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 com.google.cloud.teleport.v2.templates;
import com.google.cloud.teleport.metadata.Template;
import com.google.cloud.teleport.metadata.TemplateCategory;
import com.google.cloud.teleport.metadata.TemplateParameter;
import com.google.cloud.teleport.metadata.TemplateParameter.TemplateEnumOption;
import com.google.cloud.teleport.v2.common.UncaughtExceptionLogger;
import com.google.cloud.teleport.v2.datastream.io.CdcJdbcIO;
import com.google.cloud.teleport.v2.datastream.sources.DataStreamIO;
import com.google.cloud.teleport.v2.datastream.values.DmlInfo;
import com.google.cloud.teleport.v2.templates.DataStreamToSQL.Options;
import com.google.cloud.teleport.v2.transforms.CreateDml;
import com.google.cloud.teleport.v2.transforms.ProcessDml;
import com.google.cloud.teleport.v2.values.FailsafeElement;
import com.google.common.base.Splitter;
import java.sql.SQLException;
import java.util.HashMap;
import java.util.Map;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.StreamingOptions;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* This pipeline ingests DataStream data from GCS. The data is then cleaned and converted from JSON
* objects into DML statements. The DML is applied to the desired target database, which can be one
* of MySQL or PostgreSQL. Replication maintains a 1:1 match between source and target by default.
* No DDL is supported in the current version of this pipeline.
*
* <p>NOTE: Future versions will support: Pub/Sub, GCS, or Kafka as per DataStream
*
* <p>Check out <a
* href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/datastream-to-sql/README_Cloud_Datastream_to_SQL.md">README</a>
* for instructions on how to use or modify this template.
*/
@Template(
name = "Cloud_Datastream_to_SQL",
category = TemplateCategory.STREAMING,
displayName = "Datastream to SQL",
description = {
"The Datastream to SQL template is a streaming pipeline that reads <a href=\"https://cloud.google.com/datastream/docs\">Datastream</a> data and replicates it into any MySQL or PostgreSQL database. "
+ "The template reads data from Cloud Storage using Pub/Sub notifications and replicates this data into SQL replica tables.\n",
"The template does not support data definition language (DDL) and expects that all tables already exist in the database. "
+ "Replication uses Dataflow stateful transforms to filter stale data and ensure consistency in out of order data. "
+ "For example, if a more recent version of a row has already passed through, a late arriving version of that row is ignored. "
+ "The data manipulation language (DML) that executes is a best attempt to perfectly replicate source to target data. The DML statements executed follow the following rules:\n",
"If a primary key exists, insert and update operations use upsert syntax (ie. <code>INSERT INTO table VALUES (...) ON CONFLICT (...) DO UPDATE</code>).\n"
+ "If primary keys exist, deletes are replicated as a delete DML.\n"
+ "If no primary key exists, both insert and update operations are inserted into the table.\n"
+ "If no primary keys exist, deletes are ignored.\n"
+ "If you are using the Oracle to Postgres utilities, add <code>ROWID</code> in SQL as the primary key when none exists."
},
optionsClass = Options.class,
flexContainerName = "datastream-to-sql",
documentation =
"https://cloud.google.com/dataflow/docs/guides/templates/provided/datastream-to-sql",
contactInformation = "https://cloud.google.com/support",
preview = true,
requirements = {
"A Datastream stream that is ready to or already replicating data.",
"<a href=\"https://cloud.google.com/storage/docs/reporting-changes\">Cloud Storage Pub/Sub notifications</a> are enabled for the Datastream data.",
"A PostgreSQL database was seeded with the required schema.",
"Network access between Dataflow workers and PostgreSQL is set up."
},
streaming = true,
supportsAtLeastOnce = true)
public class DataStreamToSQL {
private static final Logger LOG = LoggerFactory.getLogger(DataStreamToSQL.class);
private static final String AVRO_SUFFIX = "avro";
private static final String JSON_SUFFIX = "json";
/**
* Options supported by the pipeline.
*
* <p>Inherits standard configuration options.
*/
public interface Options extends PipelineOptions, StreamingOptions {
@TemplateParameter.GcsReadFile(
order = 1,
groupName = "Source",
description = "File location for Datastream file input in Cloud Storage.",
helpText =
"The file location for the Datastream files in Cloud Storage to replicate. This file location is typically the root path for the stream.")
String getInputFilePattern();
void setInputFilePattern(String value);
@TemplateParameter.PubsubSubscription(
order = 2,
optional = true,
description = "The Pub/Sub subscription being used in a Cloud Storage notification policy.",
helpText =
"The Pub/Sub subscription with Datastream file notifications."
+ " For example, `projects/<PROJECT_ID>/subscriptions/<SUBSCRIPTION_ID>`.")
String getGcsPubSubSubscription();
void setGcsPubSubSubscription(String value);
@TemplateParameter.Enum(
order = 3,
enumOptions = {@TemplateEnumOption("avro"), @TemplateEnumOption("json")},
optional = true,
description = "Datastream output file format (avro/json).",
helpText =
"The format of the output file produced by Datastream. For example, `avro` or `json`. Defaults to `avro`.")
@Default.String("avro")
String getInputFileFormat();
void setInputFileFormat(String value);
@TemplateParameter.Text(
order = 4,
groupName = "Source",
optional = true,
description = "Name or template for the stream to poll for schema information.",
helpText =
"The name or template for the stream to poll for schema information. The default value is `{_metadata_stream}`.")
String getStreamName();
void setStreamName(String value);
@TemplateParameter.DateTime(
order = 5,
optional = true,
description =
"The starting DateTime used to fetch from Cloud Storage "
+ "(https://tools.ietf.org/html/rfc3339).",
helpText =
"The starting DateTime used to fetch from Cloud Storage "
+ "(https://tools.ietf.org/html/rfc3339).")
@Default.String("1970-01-01T00:00:00.00Z")
String getRfcStartDateTime();
void setRfcStartDateTime(String value);
// DataStream API Root Url (only used for testing)
@TemplateParameter.Text(
order = 6,
optional = true,
description = "Datastream API Root URL (only required for testing)",
helpText = "Datastream API Root URL")
@Default.String("https://datastream.googleapis.com/")
String getDataStreamRootUrl();
void setDataStreamRootUrl(String value);
// SQL Connection Parameters
@TemplateParameter.Enum(
order = 7,
optional = true,
enumOptions = {@TemplateEnumOption("postgres"), @TemplateEnumOption("mysql")},
description = "SQL Database Type (postgres or mysql).",
helpText = "The database type to write to (for example, Postgres).")
@Default.String("postgres")
String getDatabaseType();
void setDatabaseType(String value);
@TemplateParameter.Text(
order = 8,
groupName = "Target",
description = "Database Host to connect on.",
helpText = "The SQL host to connect on.")
String getDatabaseHost();
void setDatabaseHost(String value);
@TemplateParameter.Text(
order = 9,
groupName = "Target",
optional = true,
description = "Database Port to connect on.",
helpText = "The SQL database port to connect to. The default value is `5432`.")
@Default.String("5432")
String getDatabasePort();
void setDatabasePort(String value);
@TemplateParameter.Text(
order = 10,
description = "Database User to connect with.",
helpText =
"The SQL user with all required permissions to write to all tables in replication.")
String getDatabaseUser();
void setDatabaseUser(String value);
@TemplateParameter.Password(
order = 11,
description = "Database Password for given user.",
helpText = "The password for the SQL user.")
String getDatabasePassword();
void setDatabasePassword(String value);
@TemplateParameter.Text(
order = 12,
groupName = "Target",
optional = true,
description = "SQL Database Name.",
helpText = "The name of the SQL database to connect to. The default value is `postgres`.")
@Default.String("postgres")
String getDatabaseName();
void setDatabaseName(String value);
@TemplateParameter.Text(
order = 13,
optional = true,
description = "A map of key/values used to dictate schema name changes",
helpText =
"A map of key/values used to dictate schema name changes (ie."
+ " old_name:new_name,CaseError:case_error)")
@Default.String("")
String getSchemaMap();
void setSchemaMap(String value);
@TemplateParameter.Text(
order = 14,
groupName = "Target",
optional = true,
description = "Custom connection string.",
helpText =
"Optional connection string which will be used instead of the default database string.")
@Default.String("")
String getCustomConnectionString();
void setCustomConnectionString(String value);
@TemplateParameter.Integer(
order = 15,
optional = true,
description = "Number of threads to use for Format to DML step.",
helpText =
"Determines key parallelism of Format to DML step, specifically, the value is passed into Reshuffle.withNumBuckets.")
@Default.Integer(100)
int getNumThreads();
void setNumThreads(int value);
}
/**
* Main entry point for executing the pipeline.
*
* @param args The command-line arguments to the pipeline.
*/
public static void main(String[] args) {
UncaughtExceptionLogger.register();
LOG.info("Starting Datastream to SQL");
Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
options.setStreaming(true);
run(options);
}
/**
* Build the DataSourceConfiguration for the target SQL database. Using the pipeline options,
* determine the database type and create the correct jdbc connection for the requested DB.
*
* @param options The execution parameters to the pipeline.
*/
public static CdcJdbcIO.DataSourceConfiguration getDataSourceConfiguration(Options options) {
String jdbcDriverName;
String jdbcDriverConnectionString;
switch (options.getDatabaseType()) {
case "postgres":
jdbcDriverName = "org.postgresql.Driver";
jdbcDriverConnectionString =
String.format(
"jdbc:postgresql://%s:%s/%s",
options.getDatabaseHost(), options.getDatabasePort(), options.getDatabaseName());
break;
case "mysql":
jdbcDriverName = "com.mysql.cj.jdbc.Driver";
jdbcDriverConnectionString =
String.format(
"jdbc:mysql://%s:%s/%s",
options.getDatabaseHost(), options.getDatabasePort(), options.getDatabaseName());
break;
default:
throw new IllegalArgumentException(
String.format("Database Type %s is not supported.", options.getDatabaseType()));
}
if (!options.getCustomConnectionString().isEmpty()) {
jdbcDriverConnectionString = options.getCustomConnectionString();
}
CdcJdbcIO.DataSourceConfiguration dataSourceConfiguration =
CdcJdbcIO.DataSourceConfiguration.create(jdbcDriverName, jdbcDriverConnectionString)
.withUsername(options.getDatabaseUser())
.withPassword(options.getDatabasePassword())
.withMaxIdleConnections(new Integer(0));
return dataSourceConfiguration;
}
/**
* Validate the options supplied match expected values. We will also validate that connectivity is
* working correctly for the target SQL database.
*
* @param options The execution parameters to the pipeline.
* @param dataSourceConfiguration The JDBC datasource configuration.
*/
public static void validateOptions(
Options options, CdcJdbcIO.DataSourceConfiguration dataSourceConfiguration) {
try {
if (options.getDatabaseHost() != null) {
dataSourceConfiguration.buildDatasource().getConnection().close();
}
} catch (SQLException e) {
throw new IllegalArgumentException(e);
}
}
/** Parse the SchemaMap config which allows key:value pairs of column naming configs. */
public static Map<String, String> parseSchemaMap(String schemaMapString) {
if (schemaMapString == null || schemaMapString.equals("")) {
return new HashMap<>();
}
return Splitter.on(",").withKeyValueSeparator(":").split(schemaMapString);
}
/**
* Runs the pipeline with the supplied options.
*
* @param options The execution parameters to the pipeline.
* @return The result of the pipeline execution.
*/
public static PipelineResult run(Options options) {
/*
* Stages:
* 1) Ingest and Normalize Data to FailsafeElement with JSON Strings
* 2) Write JSON Strings to SQL DML Objects
* 3) Filter stale rows using stateful PK transform
* 4) Write DML statements to SQL Database via jdbc
*/
Pipeline pipeline = Pipeline.create(options);
CdcJdbcIO.DataSourceConfiguration dataSourceConfiguration = getDataSourceConfiguration(options);
validateOptions(options, dataSourceConfiguration);
Map<String, String> schemaMap = parseSchemaMap(options.getSchemaMap());
LOG.info("Parsed schema map: {}", schemaMap);
/*
* Stage 1: Ingest and Normalize Data to FailsafeElement with JSON Strings
* a) Read DataStream data from GCS into JSON String FailsafeElements (datastreamJsonRecords)
*/
PCollection<FailsafeElement<String, String>> datastreamJsonRecords =
pipeline.apply(
new DataStreamIO(
options.getStreamName(),
options.getInputFilePattern(),
options.getInputFileFormat(),
options.getGcsPubSubSubscription(),
options.getRfcStartDateTime())
.withLowercaseSourceColumns()
.withRenameColumnValue("_metadata_row_id", "rowid")
.withHashRowId());
/*
* Stage 2: Write JSON Strings to SQL Insert Strings
* a) Convert JSON String FailsafeElements to TableRow's (tableRowRecords)
* Stage 3) Filter stale rows using stateful PK transform
*/
PCollection<KV<String, DmlInfo>> dmlStatements =
datastreamJsonRecords
.apply(
"Format to DML",
CreateDml.of(dataSourceConfiguration)
.withSchemaMap(schemaMap)
.withNumThreads(options.getNumThreads()))
.apply("DML Stateful Processing", ProcessDml.statefulOrderByPK());
/*
* Stage 4: Write Inserts to CloudSQL
*/
dmlStatements.apply(
"Write to SQL",
CdcJdbcIO.<KV<String, DmlInfo>>write()
.withDataSourceConfiguration(dataSourceConfiguration)
.withStatementFormatter(
new CdcJdbcIO.StatementFormatter<KV<String, DmlInfo>>() {
public String formatStatement(KV<String, DmlInfo> element) {
LOG.debug("Executing SQL: {}", element.getValue().getDmlSql());
return element.getValue().getDmlSql();
}
}));
// Execute the pipeline and return the result.
return pipeline.run();
}
}
