Execute read operations in sequence - Apache Beam - google-cloud-dataflow

I need to execute below operations in sequence as given:-
PCollection<String> read = p.apply("Read Lines",TextIO.read().from(options.getInputFile()))
.apply("Get fileName",ParDo.of(new DoFn<String,String>(){
ValueProvider<String> fileReceived = options.getfilename();
#ProcessElement
public void procesElement(ProcessContext c)
{
fileName = fileReceived.get().toString();
LOG.info("File: "+fileName);
}
}));
PCollection<TableRow> rows = p.apply("Read from BigQuery",
BigQueryIO.read()
.fromQuery("SELECT table,schema FROM `DatasetID.TableID` WHERE file='" + fileName +"'")
.usingStandardSql());
How to accomplish this in Apache Beam/Dataflow?

It seems that you want to apply BigQueryIO.read().fromQuery() to a query that depends on a value available via a property of type ValueProvider<String> in your PipelineOptions, and the provider is not accessible at pipeline construction time - i.e. you are invoking your job via a template.
In that case, the proper solution is to use NestedValueProvider:
PCollection<TableRow> tableRows = p.apply(BigQueryIO.read().fromQuery(
NestedValueProvider.of(
options.getfilename(),
new SerializableFunction<String, String>() {
#Override
public String apply(String filename) {
return "SELECT table,schema FROM `DatasetID.TableID` WHERE file='" + fileName +"'";
}
})));

Related

Can I use setWorkerCacheMb in Apache Beam 2.0+?

My Dataflow job (using Java SDK 2.1.0) is quite slow and it is going to take more than a day to process just 50GB. I just pull a whole table from BigQuery (50GB), join with one csv file from GCS (100+MB).
https://cloud.google.com/dataflow/model/group-by-key
I use sideInputs to perform join (the latter way in the documentation above) while I think using CoGroupByKey is more efficient, however I'm not sure that is the only reason my job is super slow.
I googled and it looks by default, a cache of sideinputs set as 100MB and I assume my one is slightly over that limit then each worker continuously re-reads sideinputs. To improve it, I thought I can use setWorkerCacheMb method to increase the cache size.
However it looks DataflowPipelineOptions does not have this method and DataflowWorkerHarnessOptions is hidden. Just passing --workerCacheMb=200 in -Dexec.args results in
An exception occured while executing the Java class.
null: InvocationTargetException:
Class interface com.xxx.yyy.zzz$MyOptions missing a property
named 'workerCacheMb'. -> [Help 1]
How can I use this option? Thanks.
My pipeline:
MyOptions options = PipelineOptionsFactory.fromArgs(args).withValidation().as(MyOptions.class);
Pipeline p = Pipeline.create(options);
PCollection<TableRow> rows = p.apply("Read from BigQuery",
BigQueryIO.read().from("project:MYDATA.events"));
// Read account file
PCollection<String> accounts = p.apply("Read from account file",
TextIO.read().from("gs://my-bucket/accounts.csv")
.withCompressionType(CompressionType.GZIP));
PCollection<TableRow> accountRows = accounts.apply("Convert to TableRow",
ParDo.of(new DoFn<String, TableRow>() {
private static final long serialVersionUID = 1L;
#ProcessElement
public void processElement(ProcessContext c) throws Exception {
String line = c.element();
CSVParser csvParser = new CSVParser();
String[] fields = csvParser.parseLine(line);
TableRow row = new TableRow();
row = row.set("account_id", fields[0]).set("account_uid", fields[1]);
c.output(row);
}
}));
PCollection<KV<String, TableRow>> kvAccounts = accountRows.apply("Populate account_uid:accounts KV",
ParDo.of(new DoFn<TableRow, KV<String, TableRow>>() {
private static final long serialVersionUID = 1L;
#ProcessElement
public void processElement(ProcessContext c) throws Exception {
TableRow row = c.element();
String uid = (String) row.get("account_uid");
c.output(KV.of(uid, row));
}
}));
final PCollectionView<Map<String, TableRow>> uidAccountView = kvAccounts.apply(View.<String, TableRow>asMap());
// Add account_id from account_uid to event data
PCollection<TableRow> rowsWithAccountID = rows.apply("Join account_id",
ParDo.of(new DoFn<TableRow, TableRow>() {
private static final long serialVersionUID = 1L;
#ProcessElement
public void processElement(ProcessContext c) throws Exception {
TableRow row = c.element();
if (row.containsKey("account_uid") && row.get("account_uid") != null) {
String uid = (String) row.get("account_uid");
TableRow accRow = (TableRow) c.sideInput(uidAccountView).get(uid);
if (accRow == null) {
LOG.warn("accRow null, {}", row.toPrettyString());
} else {
row = row.set("account_id", accRow.get("account_id"));
}
}
c.output(row);
}
}).withSideInputs(uidAccountView));
// Insert into BigQuery
WriteResult result = rowsWithAccountID.apply(BigQueryIO.writeTableRows()
.to(new TableRefPartition(StaticValueProvider.of("MYDATA"), StaticValueProvider.of("dev"),
StaticValueProvider.of("deadletter_bucket")))
.withFormatFunction(new SerializableFunction<TableRow, TableRow>() {
private static final long serialVersionUID = 1L;
#Override
public TableRow apply(TableRow row) {
return row;
}
}).withCreateDisposition(CreateDisposition.CREATE_NEVER)
.withWriteDisposition(WriteDisposition.WRITE_APPEND));
p.run();
Historically my system have two identifiers of users, new one (account_id) and old one(account_uid). Now I need to add new account_id to our event data stored in BigQuery retroactively, because old data only has old account_uid. Accounts table (which has relation between account_uid and account_id) is already converted as csv and stored in GCS.
The last func TableRefPartition just store data into BQ's corresponding partition depending on each event timestamp. The job is still running (2017-10-30_22_45_59-18169851018279768913) and bottleneck looks Join account_id part.
That part of throughput (xxx elements/s) goes up and down according to the graph. According to the graph, estimated size of sideInputs is 106MB.
If switching to CoGroupByKey improves performance dramatically, I will do so. I was just lazy and thought using sideInputs is easier to handle event data which doesn't have account info as well.
Try one of:
1) setting the option using some code:
options.as(DataflowWorkerHarnessOptions.class).setWorkerCacheMb(500);
2) having your application register DataflowWorkerHarnessOptions with the PipelineOptionsFactory
3) Having your own options class extend DataflowWorkerHarnessOptions
There's a few things you can do to improve the performance of your code:
Your side input is a Map<String, TableRow>, but you're using only a single field in the TableRow - accRow.get("account_id"). How about making it a Map<String, String> instead, having the value be the account_id itself? That'll likely be quite a bit more efficient than the bulky TableRow object.
You could extract the value of the side input into a lazily initialized member variable in your DoFn, to avoid repeated invocations of .sideInput().
That said, this performance is unexpected and we are investigating whether there's something else going on.

DymanicDestinations in Apache Beam

I have a PCollection [String] say "X" that I need to dump in a BigQuery table.
The table destination and the schema for it is in a PCollection[TableRow] say "Y".
How to accomplish this in the simplest manner?
I tried extracting the table and schema from "Y" and saving it in static global variables (tableName and schema respectively). But somehow oddly the BigQueryIO.writeTableRows() always gets the value of the variable tableName as null. But it gets the schema. I tried logging the values of those variables and I can see the values are there for both.
Here is my pipeline code:
static String tableName;
static TableSchema schema;
PCollection<String> read = p.apply("Read from input file",
TextIO.read().from(options.getInputFile()));
PCollection<TableRow> tableRows = p.apply(
BigQueryIO.read().fromQuery(NestedValueProvider.of(
options.getfilename(),
new SerializableFunction<String, String>() {
#Override
public String apply(String filename) {
return "SELECT table,schema FROM `BigqueryTest.configuration` WHERE file='" + filename +"'";
}
})).usingStandardSql().withoutValidation());
final PCollectionView<List<String>> dataView = read.apply(View.asList());
tableRows.apply("Convert data read from file to TableRow",
ParDo.of(new DoFn<TableRow,TableRow>(){
#ProcessElement
public void processElement(ProcessContext c) {
tableName = c.element().get("table").toString();
String[] schemas = c.element().get("schema").toString().split(",");
List<TableFieldSchema> fields = new ArrayList<>();
for(int i=0;i<schemas.length;i++) {
fields.add(new TableFieldSchema()
.setName(schemas[i].split(":")[0]).setType(schemas[i].split(":")[1]));
}
schema = new TableSchema().setFields(fields);
//My code to convert data to TableRow format.
}}).withSideInputs(dataView));
tableRows.apply("write to BigQuery",
BigQueryIO.writeTableRows()
.withSchema(schema)
.to("ProjectID:DatasetID."+tableName)
.withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_TRUNCATE)
.withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED));
Everything works fine. Only BigQueryIO.write operation fails and I get the error TableId is null.
I also tried using SerializableFunction and returning the value from there but i still get null.
Here is the code that I tried for it:
tableRows.apply("write to BigQuery",
BigQueryIO.writeTableRows()
.withSchema(schema)
.to(new GetTable(tableName))
.withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_TRUNCATE)
.withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED));
public static class GetTable implements SerializableFunction<String,String> {
String table;
public GetTable() {
this.table = tableName;
}
#Override
public String apply(String arg0) {
return "ProjectId:DatasetId."+table;
}
}
I also tried using DynamicDestinations but I get an error saying schema is not provided. Honestly I'm new to the concept of DynamicDestinations and I'm not sure that I'm doing it correctly.
Here is the code that I tried for it:
tableRows2.apply(BigQueryIO.writeTableRows()
.to(new DynamicDestinations<TableRow, TableRow>() {
private static final long serialVersionUID = 1L;
#Override
public TableDestination getTable(TableRow dest) {
List<TableRow> list = sideInput(bqDataView); //bqDataView contains table and schema
String table = list.get(0).get("table").toString();
String tableSpec = "ProjectId:DatasetId."+table;
String tableDescription = "";
return new TableDestination(tableSpec, tableDescription);
}
public String getSideInputs(PCollectionView<List<TableRow>> bqDataView) {
return null;
}
#Override
public TableSchema getSchema(TableRow destination) {
return schema; //schema is getting added from the global variable
}
#Override
public TableRow getDestination(ValueInSingleWindow<TableRow> element) {
return null;
}
}.getSideInputs(bqDataView)));
Please let me know what I'm doing wrong and which path I should take.
Thank You.
Part of the reason your having trouble is because of the two stages of pipeline execution. First the pipeline is constructed on your machine. This is when all of the applications of PTransforms occur. In your first example, this is when the following lines are executed:
BigQueryIO.writeTableRows()
.withSchema(schema)
.to("ProjectID:DatasetID."+tableName)
The code within a ParDo however runs when your pipeline executes, and it does so on many machines. So the following code runs much later than the pipeline construction:
#ProcessElement
public void processElement(ProcessContext c) {
tableName = c.element().get("table").toString();
...
schema = new TableSchema().setFields(fields);
...
}
This means that neither the tableName nor the schema fields will be set at when the BigQueryIO sink is created.
Your idea to use DynamicDestinations is correct, but you need to move the code to actually generate the schema the destination into that class, rather than relying on global variables that aren't available on all of the machines.

Dataflow Map side-input issue

I'm having trouble creating a Map PCollectionView with the DataflowRunner.
The pipeline below aggregates an unbouded countingInput together with values from a side-input (containing 10 generated values).
When running the pipeline on gcp it get's stuck inside the View.asMap() transform.
More specifially, the ParDo(StreamingPCollectionViewWriter) does not have any output.
I tried this with dataflow 2.0.0-beta3, as well as with beam-0.7.0-SNAPSHOT, without any result. Note that my pipeline is running without any problem when using the local DirectRunner.
Am I doing something wrong?
All help is appreciated, thanks in advance for helping me out!
public class SimpleSideInputPipeline {
private static final Logger LOG = LoggerFactory.getLogger(SimpleSideInputPipeline.class);
public interface Options extends DataflowPipelineOptions {}
public static void main(String[] args) throws IOException {
Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
Pipeline pipeline = Pipeline.create(options);
final PCollectionView<Map<Integer, String>> sideInput = pipeline
.apply(CountingInput.forSubrange(0L, 10L))
.apply("Create KV<Integer, String>",ParDo.of(new DoFn<Long, KV<Integer, String>>() {
#ProcessElement
public void processElement(ProcessContext c) {
c.output(KV.of(c.element().intValue(), "TEST"));
}
}))
.apply(View.asMap());
pipeline
.apply(CountingInput.unbounded().withRate(1, Duration.standardSeconds(5)))
.apply("Aggregate with side-input",ParDo.of(new DoFn<Long, KV<Long, String>>() {
#ProcessElement
public void processElement(ProcessContext c) {
Map<Integer, String> map = c.sideInput(sideInput);
//get first segment from map
Object[] values = map.values().toArray();
String firstVal = (String) values[0];
LOG.info("Combined: K: "+ c.element() + " V: " + firstVal + " MapSize: " + map.size());
c.output(KV.of(c.element(), firstVal));
}
}).withSideInputs(sideInput));
pipeline.run();
}
}
No need to worry that the ParDo(StreamingPCollectionViewWriterFn) does not record any output - what it does is actually write each element to an internal location.
You code looks OK to me, and this should be investigated. I have filed BEAM-2155.

Stateful ParDo not working on Dataflow Runner

Based on Javadocs and the blog post at https://beam.apache.org/blog/2017/02/13/stateful-processing.html, I tried using a simple de-duplication example using 2.0.0-beta-2 SDK which reads a file from GCS (containing a list of jsons each with a user_id field) and then running it through a pipeline as explained below.
The input data contains about 146K events of which only 50 events are unique. The entire input is about 50MB which should be processable in considerably less time than the 2 min Fixed window. I just placed a window there to make sure the per-key-per-window semantics hold without using a GlobalWindow. I run the windowed data through 3 parallel stages to compare the results, each of which are explained below.
just copies the contents into a new file on GCS - this ensures all the events were being processed as expected and I verified the contents are exactly the same as input
Combine.PerKey on the user_id and pick only the first element from the Iterable - this essentially should deduplicate the data and it works as expected. The resulting file has the exact number of unique items from the original list of events - 50 elements
stateful ParDo which checks if the key has been seen already and emits an output only when its not. Ideally, the result from this should match the deduped data as [2] but all I am seeing is only 3 unique events. These 3 unique events always point to the same 3 user_ids in a few runs I did.
Interestingly, when I just switch from the DataflowRunner to the DirectRunner running this whole process locally, I see that the output from [3] matches [2] having only 50 unique elements as expected. So, I am doubting if there are any issues with the DataflowRunner for the Stateful ParDo.
public class StatefulParDoSample {
private static Logger logger = LoggerFactory.getLogger(StatefulParDoSample.class.getName());
static class StatefulDoFn extends DoFn<KV<String, String>, String> {
final Aggregator<Long, Long> processedElements = createAggregator("processed", Sum.ofLongs());
final Aggregator<Long, Long> skippedElements = createAggregator("skipped", Sum.ofLongs());
#StateId("keyTracker")
private final StateSpec<Object, ValueState<Integer>> keyTrackerSpec =
StateSpecs.value(VarIntCoder.of());
#ProcessElement
public void processElement(
ProcessContext context,
#StateId("keyTracker") ValueState<Integer> keyTracker) {
processedElements.addValue(1l);
final String userId = context.element().getKey();
int wasSeen = firstNonNull(keyTracker.read(), 0);
if (wasSeen == 0) {
keyTracker.write( 1);
context.output(context.element().getValue());
} else {
keyTracker.write(wasSeen + 1);
skippedElements.addValue(1l);
}
}
}
public static void main(String[] args) {
DataflowPipelineOptions pipelineOptions = PipelineOptionsFactory.create().as(DataflowPipelineOptions.class);
pipelineOptions.setRunner(DataflowRunner.class);
pipelineOptions.setProject("project-name");
pipelineOptions.setStagingLocation(GCS_STAGING_LOCATION);
pipelineOptions.setStreaming(false);
pipelineOptions.setAppName("deduper");
Pipeline p = Pipeline.create(pipelineOptions);
final ObjectMapper mapper = new ObjectMapper();
PCollection<KV<String, String>> keyedEvents =
p
.apply(TextIO.Read.from(GCS_SAMPLE_INPUT_FILE_PATH))
.apply(WithKeys.of(new SerializableFunction<String, String>() {
#Override
public String apply(String input) {
try {
Map<String, Object> eventJson =
mapper.readValue(input, Map.class);
return (String) eventJson.get("user_id");
} catch (Exception e) {
}
return "";
}
}))
.apply(
Window.into(
FixedWindows.of(Duration.standardMinutes(2))
)
);
keyedEvents
.apply(ParDo.of(new StatefulDoFn()))
.apply(TextIO.Write.to(GCS_SAMPLE_OUTPUT_FILE_PATH).withNumShards(1));
keyedEvents
.apply(Values.create())
.apply(TextIO.Write.to(GCS_SAMPLE_COPY_FILE_PATH).withNumShards(1));
keyedEvents
.apply(Combine.perKey(new SerializableFunction<Iterable<String>, String>() {
#Override
public String apply(Iterable<String> input) {
return !input.iterator().hasNext() ? "empty" : input.iterator().next();
}
}))
.apply(Values.create())
.apply(TextIO.Write.to(GCS_SAMPLE_COMBINE_FILE_PATH).withNumShards(1));
PipelineResult result = p.run();
result.waitUntilFinish();
}
}
This was a bug in the Dataflow service in batch mode, fixed in the upcoming 0.6.0 Beam release (or HEAD if you track the bleeding edge).
Thank you for bringing it to my attention! For reference, or if anything else comes up, this was tracked by BEAM-1611.

Sharing BigTable Connection object among DataFlow DoFn sub-classes

I am setting up a Java Pipeline in DataFlow to read a .csv file and to create a bunch of BigTable rows based on the content of the file. I see in the BigTable documentation the note that connecting to BigTable is an 'expensive' operation and that it's a good idea to do it only once and to share the connection among the functions that need it.
However, if I declare the Connection object as a public static variable in the main class and first connect to BigTable in the main function, I get the NullPointerException when I subsequently try to reference the connection in instances of DoFn sub-classes' processElement() function as part of my DataFlow pipeline.
Conversely, if I declare the Connection as a static variable in the actual DoFn class, then the operation works successfully.
What is the best-practice or optimal way to do this?
I'm concerned that if I implement the second option at scale, I will be wasting a lot of time and resources. If I keep the variable as static in the DoFn class, is it enough to ensure that the APIs don't try to re-establish the connection every time?
I realize there is a special BigTable I/O call to sync DataFlow pipeline objects with BigTable, but I think I need to write one on my own to build-in some special logic into the DoFn processElement() function...
This is what the "working" code looks like:
class DigitizeBT extends DoFn<String, String>{
private static Connection m_locConn;
#Override
public void processElement(ProcessContext c)
{
try
{
m_locConn = BigtableConfiguration.connect("projectID", "instanceID");
Table tbl = m_locConn.getTable(TableName.valueOf("TableName"));
Put put = new Put(Bytes.toBytes(rowKey));
put.addColumn(
Bytes.toBytes("CF1"),
Bytes.toBytes("SomeName"),
Bytes.toBytes("SomeValue"));
tbl.put(put);
}
catch (IOException e)
{
e.printStackTrace();
System.exit(1);
}
}
}
This is what updated code looks like, FYI:
public void SmallKVJob()
{
CloudBigtableScanConfiguration config = new CloudBigtableScanConfiguration.Builder()
.withProjectId(DEF.ID_PROJ)
.withInstanceId(DEF.ID_INST)
.withTableId(DEF.ID_TBL_UNITS)
.build();
DataflowPipelineOptions options = PipelineOptionsFactory.as(DataflowPipelineOptions.class);
options.setProject(DEF.ID_PROJ);
options.setStagingLocation(DEF.ID_STG_LOC);
// options.setNumWorkers(3);
// options.setMaxNumWorkers(5);
// options.setRunner(BlockingDataflowPipelineRunner.class);
options.setRunner(DirectPipelineRunner.class);
Pipeline p = Pipeline.create(options);
p.apply(TextIO.Read.from(DEF.ID_BAL))
.apply(ParDo.of(new DoFn1()))
.apply(ParDo.of(new DoFn2()))
.apply(ParDo.of(new DoFn3(config)));
m_log.info("starting to run the job");
p.run();
m_log.info("finished running the job");
}
}
class DoFn1 extends DoFn<String, KV<String, Integer>>
{
#Override
public void processElement(ProcessContext c)
{
c.output(KV.of(c.element().split("\\,")[0],Integer.valueOf(c.element().split("\\,")[1])));
}
}
class DoFn2 extends DoFn<KV<String, Integer>, KV<String, Integer>>
{
#Override
public void processElement(ProcessContext c)
{
int max = c.element().getValue();
String name = c.element().getKey();
for(int i = 0; i<max;i++)
c.output(KV.of(name, 1));
}
}
class DoFn3 extends AbstractCloudBigtableTableDoFn<KV<String, Integer>, String>
{
public DoFn3(CloudBigtableConfiguration config)
{
super(config);
}
#Override
public void processElement(ProcessContext c)
{
try
{
Integer max = c.element().getValue();
for(int i = 0; i<max; i++)
{
String owner = c.element().getKey();
String rnd = UUID.randomUUID().toString();
Put p = new Put(Bytes.toBytes(owner+"*"+rnd));
p.addColumn(Bytes.toBytes(DEF.ID_CF1), Bytes.toBytes("Owner"), Bytes.toBytes(owner));
getConnection().getTable(TableName.valueOf(DEF.ID_TBL_UNITS)).put(p);
c.output("Success");
}
} catch (IOException e)
{
c.output(e.toString());
e.printStackTrace();
}
}
}
The input .csv file looks something like this:
Mary,3000
John,5000
Peter,2000
So, for each row in the .csv file, I have to put in x number of rows into BigTable, where x is the second cell in the .csv file...
We built AbstractCloudBigtableTableDoFn ( Source & Docs ) for this purpose. Extend that class instead of DoFn, and call getConnection() instead of creating a Connection yourself.
10,000 small rows should take a second or two of actual work.
EDIT: As per the comments, BufferedMutator should be used instead of Table.put() for best throughput.

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