Google Cloud to Neo4j 模板可让您通过 Dataflow 作业将数据集导入 Neo4j 数据库，数据来源是 Cloud Storage 存储桶中托管的 CSV 文件。还可以让您在导入的各个步骤中操作和转换数据。您可以使用该模板进行首次导入和增量导入。
流水线要求
- 正在运行的 Neo4j 实例
- Cloud Storage 存储桶
- 要导入的数据集，采用 CSV 文件形式
- 要使用的作业规范文件
创建作业规范文件
作业规范文件由一个包含以下部分的 JSON 对象组成：
config：影响导入执行方式的全局标志。sources：数据源定义（关系型）。targets：数据目标定义（图表：节点/关系）。actions：加载前/加载后操作。
如需了解详情，请参阅 Neo4j 文档中的创建作业规范文件。
模板参数
| 参数 | 说明 |
|---|---|
jobSpecUri |
作业规范文件的路径，该文件包含源和目标元数据的配置。 |
neo4jConnectionUri |
Neo4j 连接元数据 JSON 文件的路径。 |
optionsJson |
可选：选项 JSON。使用运行时令牌。例如 {token1:value1,token2:value2}。默认值为空。 |
readQuery |
可选：替换 SQL 查询。默认值为空。 |
inputFilePattern |
可选：替换文本文件模式。例如 gs://your-bucket/path/*.json。默认值为空。 |
disabledAlgorithms |
可选：要停用的算法的逗号分隔列表。如果此值设置为 none，则不会停用任何算法。请谨慎使用，因为已知默认停用的算法存在漏洞或性能问题。例如：SSLv3, RC4。 |
extraFilesToStage |
可选：用于将文件暂存在工作器中的 Cloud Storage 路径或 Secret Manager 密文，以逗号分隔。这些文件保存在每个工作器的 /extra_files 目录下。例如：gs://your-bucket/file.txt,projects/project-id/secrets/secret-id/versions/version-id。 |
运行模板
控制台
- 转到 Dataflow 基于模板创建作业页面。 转到“基于模板创建作业”
- 在作业名称字段中，输入唯一的作业名称。
- 可选：对于区域性端点，从下拉菜单中选择一个值。默认区域为
us-central1。如需查看可以在其中运行 Dataflow 作业的区域列表，请参阅 Dataflow 位置。
- 从 Dataflow 模板下拉菜单中，选择 the Google Cloud to Neo4j template。
- 在提供的参数字段中，输入您的参数值。
- 点击运行作业。
gcloud
在 shell 或终端中，运行模板：
gcloud dataflow flex-template run JOB_NAME \ --template-file-gcs-location=gs://dataflow-templates-REGION_NAME/VERSION/flex/Google_Cloud_to_Neo4j \ --project=PROJECT_ID \ --region=REGION_NAME \ --parameters \ jobSpecUri=JOB_SPEC_URI,\ neo4jConnectionUri=NEO4J_CONNECTION_URI,\
请替换以下内容：
JOB_NAME：您选择的唯一性作业名称VERSION：您要使用的模板的版本您可使用以下值：
latest，以使用模板的最新版本，该模板在存储桶的未标示日期的父文件夹 (gs://dataflow-templates-REGION_NAME/latest/) 中可用- 版本名称（如
2023-09-12-00_RC00），以使用模板的特定版本，该版本嵌套在存储桶的相应日期父文件夹 (gs://dataflow-templates-REGION_NAME/) 中
REGION_NAME：要在其中部署 Dataflow 作业的区域，例如us-central1JOB_SPEC_URI：作业规范文件的路径NEO4J_CONNECTION_URI：Neo4j 连接元数据的路径
API
如需使用 REST API 来运行模板，请发送 HTTP POST 请求。如需详细了解 API 及其授权范围，请参阅 projects.templates.launch。
POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/flexTemplates:launch { "launchParameter": { "jobName": "JOB_NAME", "parameters": { "jobSpecUri": "JOB_SPEC_URI", "neo4jConnectionUri": "NEO4J_CONNECTION_URI", }, "containerSpecGcsPath": "gs://dataflow-templates-LOCATION/VERSION/flex/Google_Cloud_to_Neo4j", "environment": { "maxWorkers": "10" } } }
请替换以下内容：
PROJECT_ID：您要在其中运行 Dataflow 作业的 Google Cloud 项目的 IDJOB_NAME：您选择的唯一性作业名称VERSION：您要使用的模板的版本您可使用以下值：
latest，以使用模板的最新版本，该模板在存储桶的未标示日期的父文件夹 (gs://dataflow-templates-REGION_NAME/latest/) 中可用- 版本名称（如
2023-09-12-00_RC00），以使用模板的特定版本，该版本嵌套在存储桶的相应日期父文件夹 (gs://dataflow-templates-REGION_NAME/) 中
LOCATION：要在其中部署 Dataflow 作业的区域，例如us-central1JOB_SPEC_URI：作业规范文件的路径NEO4J_CONNECTION_URI：Neo4j 连接元数据的路径
模板源代码
Java
/*
* Copyright (C) 2021 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.neo4j.templates;
import com.google.cloud.teleport.metadata.Template;
import com.google.cloud.teleport.metadata.Template.AdditionalDocumentationBlock;
import com.google.cloud.teleport.metadata.TemplateCategory;
import com.google.cloud.teleport.v2.common.UncaughtExceptionLogger;
import com.google.cloud.teleport.v2.neo4j.actions.ActionDoFnFactory;
import com.google.cloud.teleport.v2.neo4j.actions.ActionPreloadFactory;
import com.google.cloud.teleport.v2.neo4j.actions.preload.PreloadAction;
import com.google.cloud.teleport.v2.neo4j.database.Neo4jConnection;
import com.google.cloud.teleport.v2.neo4j.model.InputRefactoring;
import com.google.cloud.teleport.v2.neo4j.model.InputValidator;
import com.google.cloud.teleport.v2.neo4j.model.Json;
import com.google.cloud.teleport.v2.neo4j.model.Json.ParsingResult;
import com.google.cloud.teleport.v2.neo4j.model.connection.ConnectionParams;
import com.google.cloud.teleport.v2.neo4j.model.enums.ActionExecuteAfter;
import com.google.cloud.teleport.v2.neo4j.model.enums.ArtifactType;
import com.google.cloud.teleport.v2.neo4j.model.enums.TargetType;
import com.google.cloud.teleport.v2.neo4j.model.helpers.JobSpecMapper;
import com.google.cloud.teleport.v2.neo4j.model.helpers.OptionsParamsMapper;
import com.google.cloud.teleport.v2.neo4j.model.helpers.SourceQuerySpec;
import com.google.cloud.teleport.v2.neo4j.model.helpers.SourceQuerySpec.SourceQuerySpecBuilder;
import com.google.cloud.teleport.v2.neo4j.model.helpers.TargetQuerySpec;
import com.google.cloud.teleport.v2.neo4j.model.helpers.TargetQuerySpec.TargetQuerySpecBuilder;
import com.google.cloud.teleport.v2.neo4j.model.job.Action;
import com.google.cloud.teleport.v2.neo4j.model.job.ActionContext;
import com.google.cloud.teleport.v2.neo4j.model.job.JobSpec;
import com.google.cloud.teleport.v2.neo4j.model.job.OptionsParams;
import com.google.cloud.teleport.v2.neo4j.model.job.Source;
import com.google.cloud.teleport.v2.neo4j.model.job.Target;
import com.google.cloud.teleport.v2.neo4j.options.Neo4jFlexTemplateOptions;
import com.google.cloud.teleport.v2.neo4j.providers.Provider;
import com.google.cloud.teleport.v2.neo4j.providers.ProviderFactory;
import com.google.cloud.teleport.v2.neo4j.transforms.Neo4jRowWriterTransform;
import com.google.cloud.teleport.v2.neo4j.utils.BeamBlock;
import com.google.cloud.teleport.v2.neo4j.utils.FileSystemUtils;
import com.google.cloud.teleport.v2.neo4j.utils.ModelUtils;
import com.google.cloud.teleport.v2.neo4j.utils.ProcessingCoder;
import com.google.cloud.teleport.v2.utils.SecretManagerUtils;
import com.google.gson.Gson;
import com.google.gson.GsonBuilder;
import java.util.List;
import java.util.Map;
import org.apache.beam.runners.dataflow.options.DataflowPipelineOptions;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.coders.VarIntCoder;
import org.apache.beam.sdk.io.FileSystems;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.schemas.Schema;
import org.apache.beam.sdk.transforms.Create;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.Wait;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.Row;
import org.apache.beam.sdk.values.TypeDescriptor;
import org.apache.commons.lang3.StringUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* Dataflow template which reads Google Cloud data (Text, BigQuery) and writes it to Neo4j.
*
* <p>In case of BigQuery, the source data can be either a table or a SQL query.
*
* <p>Check out <a
* href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/googlecloud-to-neo4j/README_Google_Cloud_to_Neo4j.md">README</a>
* for instructions on how to use or modify this template.
*/
@Template(
name = "Google_Cloud_to_Neo4j",
category = TemplateCategory.BATCH,
displayName = "Google Cloud to Neo4j",
description =
"The Google Cloud to Neo4j template lets you import a dataset into a Neo4j database through a Dataflow job, "
+ "sourcing data from CSV files hosted in Google Cloud Storage buckets. It also lets you to manipulate and transform the data "
+ "at various steps of the import. You can use the template for both first-time imports and incremental imports.",
optionsClass = Neo4jFlexTemplateOptions.class,
flexContainerName = "googlecloud-to-neo4j",
contactInformation = "https://support.neo4j.com/",
documentation =
"https://cloud.google.com/dataflow/docs/guides/templates/provided/google-cloud-to-neo4j",
requirements = {
"A running Neo4j instance",
"A Google Cloud Storage bucket",
"A dataset to import, in the form of CSV files",
"A job specification file to use"
},
additionalDocumentation = {
@AdditionalDocumentationBlock(
name = "Create a job specification file",
content = {
"The job specification file consists of a JSON object with the following sections:\n"
+ "- `config` - global flags affecting how the import is performed.\n"
+ "- `sources` - data source definitions (relational).\n"
+ "- `targets` - data target definitions (graph: nodes/relationships).\n"
+ "- `actions` - pre/post-load actions.\n"
+ "For more information, see <a href=\"https://neo4j.com/docs/dataflow-google-cloud/job-specification/\" class=\"external\">Create a job specification file</a> in the Neo4j documentation."
})
},
preview = true)
public class GoogleCloudToNeo4j {
private static final Logger LOG = LoggerFactory.getLogger(GoogleCloudToNeo4j.class);
private static final Gson gson = new GsonBuilder().setPrettyPrinting().create();
private final OptionsParams optionsParams;
private final ConnectionParams neo4jConnection;
private final JobSpec jobSpec;
private final Pipeline pipeline;
private final String templateVersion;
/**
* Main class for template. Initializes job using run-time on pipelineOptions.
*
* @param pipelineOptions framework supplied arguments
*/
public GoogleCloudToNeo4j(Neo4jFlexTemplateOptions pipelineOptions) {
////////////////////////////
// Job name gets a date on it when running within the container, but not with DirectRunner
// final String jobName = pipelineOptions.getJobName() + "-" + System.currentTimeMillis();
// pipelineOptions.setJobName(jobName);
// Set pipeline options
this.pipeline = Pipeline.create(pipelineOptions);
FileSystems.setDefaultPipelineOptions(pipelineOptions);
this.optionsParams = OptionsParamsMapper.fromPipelineOptions(pipelineOptions);
// Validate pipeline
processValidations(
"Errors found validating pipeline options: ",
InputValidator.validateNeo4jPipelineOptions(pipelineOptions));
this.templateVersion = readTemplateVersion(pipelineOptions);
String neo4jConnectionJson = readConnectionSettings(pipelineOptions);
ParsingResult parsingResult = InputValidator.validateNeo4jConnection(neo4jConnectionJson);
if (!parsingResult.isSuccessful()) {
processValidations(
"Errors found validating Neo4j connection: ",
parsingResult.formatErrors("Could not validate connection JSON"));
}
this.neo4jConnection = Json.map(parsingResult, ConnectionParams.class);
this.jobSpec = JobSpecMapper.fromUri(pipelineOptions.getJobSpecUri());
// Validate job spec
processValidations(
"Errors found validating job specification: ",
InputValidator.validateJobSpec(this.jobSpec));
///////////////////////////////////
// Refactor job spec
InputRefactoring inputRefactoring = new InputRefactoring(this.optionsParams);
// Variable substitution
inputRefactoring.refactorJobSpec(this.jobSpec);
// Optimizations
inputRefactoring.optimizeJobSpec(this.jobSpec);
// Source specific validations
for (Source source : jobSpec.getSourceList()) {
// get provider implementation for source
Provider providerImpl = ProviderFactory.of(source.getSourceType());
providerImpl.configure(optionsParams, jobSpec);
}
// Output debug log spec
LOG.debug("Normalized JobSpec: {}", gson.toJson(this.jobSpec));
}
private static String readTemplateVersion(Neo4jFlexTemplateOptions options) {
Map<String, String> labels = options.as(DataflowPipelineOptions.class).getLabels();
String defaultVersion = "UNKNOWN";
if (labels == null) {
return defaultVersion;
}
return labels.getOrDefault("goog-dataflow-provided-template-version", defaultVersion);
}
private static String readConnectionSettings(Neo4jFlexTemplateOptions options) {
String secretId = options.getNeo4jConnectionSecretId();
if (StringUtils.isNotEmpty(secretId)) {
return SecretManagerUtils.getSecret(secretId);
}
String uri = options.getNeo4jConnectionUri();
try {
return FileSystemUtils.getPathContents(uri);
} catch (Exception e) {
throw new RuntimeException(
String.format("Unable to read Neo4j configuration at URI %s: ", uri), e);
}
}
/**
* Runs a pipeline which reads data from various sources and writes it to Neo4j.
*
* @param args arguments to the pipeline
*/
public static void main(String[] args) {
UncaughtExceptionLogger.register();
Neo4jFlexTemplateOptions options =
PipelineOptionsFactory.fromArgs(args).withValidation().as(Neo4jFlexTemplateOptions.class);
// Allow users to supply their own list of disabled algorithms if necessary
if (StringUtils.isNotBlank(options.getDisabledAlgorithms())) {
options.setDisabledAlgorithms(
"SSLv3, RC4, DES, MD5withRSA, DH keySize < 1024, EC keySize < 224, 3DES_EDE_CBC, anon,"
+ " NULL");
}
LOG.info("Job: {}", options.getJobSpecUri());
GoogleCloudToNeo4j template = new GoogleCloudToNeo4j(options);
template.run();
}
/** Raises RuntimeExceptions for validation errors. */
private void processValidations(String description, List<String> validationMessages) {
StringBuilder sb = new StringBuilder();
if (!validationMessages.isEmpty()) {
for (String msg : validationMessages) {
sb.append(msg);
sb.append(System.lineSeparator());
}
throw new RuntimeException(description + " " + sb);
}
}
public void run() {
try (Neo4jConnection directConnect =
new Neo4jConnection(this.neo4jConnection, this.templateVersion)) {
boolean resetDb = jobSpec.getConfig().getResetDb();
if (!resetDb) {
directConnect.verifyConnectivity();
} else {
directConnect.resetDatabase();
}
}
////////////////////////////
// Run preload actions
runPreloadActions(jobSpec.getPreloadActions());
////////////////////////////
// If an action transformation has no upstream PCollection, it will use this default context
PCollection<Row> defaultActionContext =
pipeline.apply(
"Default Context",
Create.empty(TypeDescriptor.of(Row.class)).withCoder(ProcessingCoder.of()));
// Creating serialization handle
BeamBlock processingQueue = new BeamBlock(defaultActionContext);
////////////////////////////
// Process sources
for (Source source : jobSpec.getSourceList()) {
// get provider implementation for source
Provider providerImpl = ProviderFactory.of(source.getSourceType());
providerImpl.configure(optionsParams, jobSpec);
PCollection<Row> sourceMetadata =
pipeline.apply(
String.format("Metadata for source %s", source.getName()),
providerImpl.queryMetadata(source));
Schema sourceBeamSchema = sourceMetadata.getSchema();
processingQueue.addToQueue(
ArtifactType.source, false, source.getName(), defaultActionContext, sourceMetadata);
PCollection<Row> nullableSourceBeamRows = null;
////////////////////////////
// Optimization: if single source query, reuse this PCollection rather than write it again
boolean targetsHaveTransforms = ModelUtils.targetsHaveTransforms(jobSpec, source);
if (!targetsHaveTransforms || !providerImpl.supportsSqlPushDown()) {
SourceQuerySpec sourceQuerySpec =
new SourceQuerySpecBuilder().source(source).sourceSchema(sourceBeamSchema).build();
nullableSourceBeamRows =
pipeline
.apply(
"Query " + source.getName(), providerImpl.querySourceBeamRows(sourceQuerySpec))
.setRowSchema(sourceBeamSchema);
}
String sourceName = source.getName();
////////////////////////////
// Optimization: if we're not mixing nodes and edges, then run in parallel
// For relationship updates, max workers should be max 2. This parameter is job configurable.
////////////////////////////
// No optimization possible so write nodes then edges.
// Write node targets
List<Target> nodeTargets =
jobSpec.getActiveTargetsBySourceAndType(sourceName, TargetType.node);
for (Target nodeTarget : nodeTargets) {
TargetQuerySpec targetQuerySpec =
new TargetQuerySpecBuilder()
.source(source)
.sourceBeamSchema(sourceBeamSchema)
.nullableSourceRows(nullableSourceBeamRows)
.target(nodeTarget)
.build();
String nodeStepDescription =
nodeTarget.getSequence()
+ ": "
+ source.getName()
+ "->"
+ nodeTarget.getName()
+ " nodes";
PCollection<Row> preInsertBeamRows =
pipeline.apply(
"Query " + nodeStepDescription, providerImpl.queryTargetBeamRows(targetQuerySpec));
Neo4jRowWriterTransform targetWriterTransform =
new Neo4jRowWriterTransform(jobSpec, neo4jConnection, templateVersion, nodeTarget);
PCollection<Row> blockingReturn =
preInsertBeamRows
.apply(
"** Unblocking "
+ nodeStepDescription
+ "(after "
+ nodeTarget.getExecuteAfter()
+ "."
+ nodeTarget.getExecuteAfterName()
+ ")",
Wait.on(
processingQueue.waitOnCollection(
nodeTarget.getExecuteAfter(),
nodeTarget.getExecuteAfterName(),
nodeStepDescription)))
.setCoder(preInsertBeamRows.getCoder())
.apply("Writing " + nodeStepDescription, targetWriterTransform)
.setCoder(preInsertBeamRows.getCoder());
processingQueue.addToQueue(
ArtifactType.node, false, nodeTarget.getName(), blockingReturn, preInsertBeamRows);
}
////////////////////////////
// Write relationship targets
List<Target> relationshipTargets =
jobSpec.getActiveTargetsBySourceAndType(sourceName, TargetType.edge);
for (Target relationshipTarget : relationshipTargets) {
TargetQuerySpec targetQuerySpec =
new TargetQuerySpecBuilder()
.source(source)
.nullableSourceRows(nullableSourceBeamRows)
.sourceBeamSchema(sourceBeamSchema)
.target(relationshipTarget)
.build();
PCollection<Row> preInsertBeamRows;
String relationshipStepDescription =
relationshipTarget.getSequence()
+ ": "
+ source.getName()
+ "->"
+ relationshipTarget.getName()
+ " edges";
if (ModelUtils.targetHasTransforms(relationshipTarget)) {
preInsertBeamRows =
pipeline.apply(
"Query " + relationshipStepDescription,
providerImpl.queryTargetBeamRows(targetQuerySpec));
} else {
preInsertBeamRows = nullableSourceBeamRows;
}
Neo4jRowWriterTransform targetWriterTransform =
new Neo4jRowWriterTransform(
jobSpec, neo4jConnection, templateVersion, relationshipTarget);
PCollection<Row> blockingReturn =
preInsertBeamRows
.apply(
"** Unblocking "
+ relationshipStepDescription
+ "(after "
+ relationshipTarget.getExecuteAfter()
+ "."
+ relationshipTarget.getExecuteAfterName()
+ ")",
Wait.on(
processingQueue.waitOnCollection(
relationshipTarget.getExecuteAfter(),
relationshipTarget.getExecuteAfterName(),
relationshipStepDescription)))
.setCoder(preInsertBeamRows.getCoder())
.apply("Writing " + relationshipStepDescription, targetWriterTransform)
.setCoder(preInsertBeamRows.getCoder());
// serialize relationships
processingQueue.addToQueue(
ArtifactType.edge,
false,
relationshipTarget.getName(),
blockingReturn,
preInsertBeamRows);
}
////////////////////////////
// Custom query targets
List<Target> customQueryTargets =
jobSpec.getActiveTargetsBySourceAndType(sourceName, TargetType.custom_query);
for (Target customQueryTarget : customQueryTargets) {
String customQueryStepDescription =
customQueryTarget.getSequence()
+ ": "
+ source.getName()
+ "->"
+ customQueryTarget.getName()
+ " (custom query)";
Neo4jRowWriterTransform targetWriterTransform =
new Neo4jRowWriterTransform(
jobSpec, neo4jConnection, templateVersion, customQueryTarget);
PCollection<Row> blockingReturn =
nullableSourceBeamRows
.apply(
"** Unblocking "
+ customQueryStepDescription
+ "(after "
+ customQueryTarget.getExecuteAfter()
+ "."
+ customQueryTarget.getExecuteAfterName()
+ ")",
Wait.on(
processingQueue.waitOnCollection(
customQueryTarget.getExecuteAfter(),
customQueryTarget.getExecuteAfterName(),
customQueryStepDescription)))
.setCoder(nullableSourceBeamRows.getCoder())
.apply("Writing " + customQueryStepDescription, targetWriterTransform)
.setCoder(nullableSourceBeamRows.getCoder());
processingQueue.addToQueue(
ArtifactType.custom_query,
false,
customQueryTarget.getName(),
blockingReturn,
nullableSourceBeamRows);
}
}
////////////////////////////
// Process actions (first pass)
runBeamActions(jobSpec.getPostloadActions(), processingQueue);
// For a Dataflow Flex Template, do NOT waitUntilFinish().
pipeline.run();
}
private void runPreloadActions(List<Action> actions) {
for (Action action : actions) {
LOG.info("Executing preload action: {}", action.name);
// Get targeted execution context
ActionContext context = new ActionContext();
context.jobSpec = this.jobSpec;
context.neo4jConnectionParams = this.neo4jConnection;
PreloadAction actionImpl = ActionPreloadFactory.of(action, context);
List<String> msgs = actionImpl.execute();
for (String msg : msgs) {
LOG.info("Preload action {}: {}", action.name, msg);
}
}
}
private void runBeamActions(List<Action> actions, BeamBlock blockingQueue) {
for (Action action : actions) {
// LOG.info("Registering action: " + gson.toJson(action));
ArtifactType artifactType = ArtifactType.action;
if (action.executeAfter == ActionExecuteAfter.source) {
artifactType = ArtifactType.source;
} else if (action.executeAfter == ActionExecuteAfter.node) {
artifactType = ArtifactType.node;
} else if (action.executeAfter == ActionExecuteAfter.edge) {
artifactType = ArtifactType.edge;
} else if (action.executeAfter == ActionExecuteAfter.custom_query) {
artifactType = ArtifactType.custom_query;
}
LOG.info("Registering action: {}", action.name);
// Get targeted execution context
PCollection<Row> executionContextCollection =
blockingQueue.getContextCollection(artifactType, action.executeAfterName);
ActionContext context = new ActionContext();
context.action = action;
context.jobSpec = this.jobSpec;
context.neo4jConnectionParams = this.neo4jConnection;
context.templateVersion = this.templateVersion;
// We have chosen a DoFn pattern applied to a single Integer row so that @ProcessElement
// evaluates only once per invocation.
// For future actions (i.e. logger) that consume upstream data context, we would use a
// Transform pattern
// The challenge in this case of the Transform pattern is that @FinishBundle could execute
// many times.
// We return <Row> from each DoFn which get rolled up into PCollection<Row> at run-time.
// A side effect of this pattern is lots of housekeeping "**" elements in the rendered DAG.
// Housekeeping elements are named for flow but not function. For instance ** Setup is
// synthetically creating a single tuple collection!
DoFn<Integer, Row> doFnActionImpl = ActionDoFnFactory.of(context);
PCollection<Row> blockingActionReturn =
pipeline
.apply("** Setup " + action.name, Create.of(1))
.apply(
"** Unblocking "
+ action.name
+ "(after "
+ action.executeAfter
+ "."
+ action.executeAfterName
+ ")",
Wait.on(
blockingQueue.waitOnCollection(
action.executeAfter, action.executeAfterName, action.name)))
.setCoder(VarIntCoder.of())
.apply("Running " + action.name, ParDo.of(doFnActionImpl))
.setCoder(executionContextCollection.getCoder());
// Add action to blocking queue since action could be a dependency.
blockingQueue.addToQueue(
ArtifactType.action,
action.executeAfter == ActionExecuteAfter.start,
action.name,
blockingActionReturn,
executionContextCollection);
}
}
}
后续步骤
- 了解 Dataflow 模板。
- 参阅 Google 提供的模板列表。