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BindiegoStreaming.java
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package bindiego;
import bindiego.BindiegoStreamingOptions;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.nio.channels.Channels;
import java.nio.channels.ReadableByteChannel;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.HashMap;
import java.util.concurrent.TimeUnit;
import java.sql.*;
// Import SLF4J packages.
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.apache.hadoop.hbase.client.Mutation;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.util.Bytes;
import com.google.common.base.Strings;
import com.google.api.services.bigquery.model.TableFieldSchema;
import com.google.api.services.bigquery.model.TableSchema;
import com.google.api.services.bigquery.model.TableReference;
import com.google.api.services.bigquery.model.TableRow;
import com.google.api.services.bigquery.model.Clustering;
import com.google.api.services.bigquery.model.TimePartitioning;
import com.google.cloud.bigtable.beam.CloudBigtableIO;
import com.google.cloud.bigtable.beam.CloudBigtableTableConfiguration;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.extensions.avro.io.AvroIO;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.CreateDisposition;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.WriteDisposition;
import org.apache.beam.sdk.io.gcp.bigquery.InsertRetryPolicy;
import org.apache.beam.sdk.io.gcp.bigquery.TableRowJsonCoder;
import org.apache.beam.sdk.io.gcp.bigquery.WriteResult;
import org.apache.beam.sdk.io.gcp.pubsub.PubsubIO;
import org.apache.beam.sdk.io.gcp.pubsub.PubsubMessage;
import org.apache.beam.sdk.io.FileBasedSink;
import org.apache.beam.sdk.io.FileSystems;
import org.apache.beam.sdk.io.fs.ResourceId;
import org.apache.beam.sdk.io.jdbc.JdbcIO;
import org.apache.beam.sdk.io.GenerateSequence;
import org.apache.beam.sdk.metrics.Counter;
import org.apache.beam.sdk.metrics.Distribution;
import org.apache.beam.sdk.metrics.Metrics;
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.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.StreamingOptions;
import org.apache.beam.sdk.options.Validation.Required;
import org.apache.beam.sdk.options.ValueProvider;
import org.apache.beam.sdk.options.ValueProvider.NestedValueProvider;
import org.apache.beam.sdk.transforms.Count;
import org.apache.beam.sdk.transforms.Sum;
import org.apache.beam.sdk.transforms.Min;
import org.apache.beam.sdk.transforms.Max;
import org.apache.beam.sdk.transforms.Mean;
import org.apache.beam.sdk.transforms.Latest;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.PTransform;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.DoFn.MultiOutputReceiver;
import org.apache.beam.sdk.transforms.DoFn.ProcessContext;
import org.apache.beam.sdk.transforms.DoFn.ProcessElement;
import org.apache.beam.sdk.transforms.SerializableFunction;
import org.apache.beam.sdk.transforms.SimpleFunction;
import org.apache.beam.sdk.transforms.windowing.Window;
import org.apache.beam.sdk.transforms.windowing.FixedWindows;
import org.apache.beam.sdk.transforms.windowing.AfterEach;
import org.apache.beam.sdk.transforms.windowing.AfterProcessingTime;
import org.apache.beam.sdk.transforms.windowing.AfterWatermark;
import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
import org.apache.beam.sdk.transforms.windowing.IntervalWindow;
import org.apache.beam.sdk.transforms.windowing.Repeatedly;
import org.apache.beam.sdk.transforms.windowing.AfterPane;
import org.apache.beam.sdk.transforms.windowing.Window.ClosingBehavior;
import org.apache.beam.sdk.transforms.windowing.GlobalWindows;
import org.apache.beam.sdk.transforms.ToString;
import org.apache.beam.sdk.transforms.WithTimestamps;
import org.apache.beam.sdk.transforms.GroupByKey;
import org.apache.beam.sdk.transforms.Combine;
import org.apache.beam.sdk.transforms.View;
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.TupleTag;
import org.apache.beam.sdk.values.TupleTagList;
import org.apache.beam.sdk.values.PCollectionView;
import org.apache.beam.sdk.extensions.avro.coders.AvroCoder;
import org.apache.beam.sdk.coders.KvCoder;
import org.apache.beam.sdk.coders.StringUtf8Coder;
import org.apache.beam.sdk.coders.Coder.Context;
import org.apache.beam.sdk.util.StreamUtils;
import org.apache.avro.Schema;
import org.apache.avro.file.CodecFactory;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;
import org.json.JSONArray;
import org.json.JSONObject;
import org.joda.time.Duration;
import org.joda.time.Instant;
import com.fasterxml.jackson.databind.ObjectMapper;
import bindiego.io.WindowedFilenamePolicy;
import bindiego.utils.DurationUtils;
import bindiego.utils.SchemaParser;
import bindiego.utils.GCSUtils;
import bindiego.io.ElasticsearchIO;
public class BindiegoStreaming {
/* extract the csv payload from message */
public static class ExtractPayload extends DoFn<PubsubMessage, String> {
public ExtractPayload(final PCollectionView<Map<String, String>> lookupTable) {
this.lookupTable = lookupTable;
}
@ProcessElement
public void processElement(ProcessContext ctx, MultiOutputReceiver r)
throws IllegalArgumentException {
// StringBuilder sb = new StringBuilder(256);
StringBuilder sb = new StringBuilder();
String payload = null;
Map<String, String> dimTable = ctx.sideInput(lookupTable);
// TODO: data validation here to prevent later various outputs inconsistency
try {
PubsubMessage psmsg = ctx.element();
// CSV format
// raw: "event_ts,thread_id,thread_name,seq,dim1,metrics1"
// after: "event_ts,thread_id,thread_name,seq,dim1,metrics1,process_ts,dim1_val"
payload = new String(psmsg.getPayload(), StandardCharsets.UTF_8);
sb.append(payload)
.append(',') // hardcoded csv delimiter
.append(Long.valueOf(System.currentTimeMillis()).toString()); // append process timestamp
// dimemsion table lookup to complement dim value
String dimVal = dimTable.get(sb.toString().split(",")[4]);
dimVal = dimVal == null ? "Not Found" : dimVal;
sb.append(',')
.append(dimVal);
logger.debug("Extracted raw message: " + sb.toString());
r.get(STR_OUT).output(sb.toString());
// use this only if the element doesn't have an event timestamp attached to it
// e.g. extract 'extractedTs' from psmsg.split(",")[0] from a CSV payload
// r.get(STR_OUT).outputWithTimestamp(str, extractedTs);
} catch (Exception ex) {
if (null == payload)
payload = "Failed to extract pubsub payload";
r.get(STR_FAILURE_OUT).output(payload);
logger.error("Failed extract pubsub message", ex);
}
}
private final PCollectionView<Map<String, String>> lookupTable;
}
/**
* Append window information to the end of csv string/row
* <...>, window, pane_info, pane_idx, pane_nonspeculative_idx,
* is_first, is_last, pane_timing, pane_event_ts
*
* FIXME: you may want to handle errors here
*/
public static class AppendWindowInfo extends DoFn<String, String> {
@ProcessElement
public void processElement(ProcessContext ctx, IntervalWindow window)
throws IllegalArgumentException {
StringBuilder sb = new StringBuilder();
try {
sb.append(ctx.element())
.append(',').append(window.toString())
.append(',').append(ctx.pane().toString())
.append(',').append(ctx.pane().getIndex())
.append(',').append(ctx.pane().getNonSpeculativeIndex())
.append(',').append(ctx.pane().isFirst())
.append(',').append(ctx.pane().isLast())
.append(',').append(ctx.pane().getTiming().toString())
.append(',').append(ctx.timestamp().getMillis());
ctx.output(sb.toString());
} catch (Exception ex) {
logger.error("Failed to append window information", ex);
}
}
}
/* add timestamp for PCollection<T> data
*
* this implementation suppose CSV and stampstamp as 1st column
*/
private static class SetTimestamp implements SerializableFunction<String, Instant> {
@Override
public Instant apply(String input) {
String[] components = input.split(","); // assume CSV
try {
return new Instant(Long.parseLong(components[0].trim())); // assume 1st column is timestamp
} catch (ArrayIndexOutOfBoundsException | NumberFormatException e) {
return Instant.now();
}
}
}
/* Convert Csv to Avro */
public static class ConvertCsvToAvro extends DoFn<String, GenericRecord> {
public ConvertCsvToAvro(String schemaJson, String delimiter) {
this.schemaJson = schemaJson;
this.delimiter = delimiter;
}
@ProcessElement
public void processElement(ProcessContext ctx) throws IllegalArgumentException {
String[] csvData = ctx.element().split(delimiter);
Schema schema = new Schema.Parser().parse(schemaJson);
// Create Avro Generic Record
GenericRecord genericRecord = new GenericData.Record(schema);
List<Schema.Field> fields = schema.getFields();
for (int index = 0; index < fields.size(); ++index) {
Schema.Field field = fields.get(index);
String fieldType = field.schema().getType().getName().toLowerCase();
// REVISIT: suprise, java can switch string :)
switch (fieldType) {
case "string":
genericRecord.put(field.name(), csvData[index]);
break;
case "boolean":
genericRecord.put(field.name(), Boolean.valueOf(csvData[index]));
break;
case "int":
genericRecord.put(field.name(), Integer.valueOf(csvData[index]));
break;
case "long":
genericRecord.put(field.name(), Long.valueOf(csvData[index]));
break;
case "float":
genericRecord.put(field.name(), Float.valueOf(csvData[index]));
break;
case "double":
genericRecord.put(field.name(), Double.valueOf(csvData[index]));
break;
default:
throw new IllegalArgumentException("Field type "
+ fieldType + " is not supported.");
}
}
ctx.output(genericRecord);
}
private String schemaJson;
private String delimiter;
}
// Read JDBC lookup table
// Why: Above service will return a PCollection, result the caller to produce a PCollection<PCollection<...>>
// when do p.apply(...)
//
// REVISIT: cheap & nasty only for demo purpose
public static class BindiegoJdbcServiceExternal {
public static Map<String, String> read(final String jdbcClass, final String jdbcConn,
final String jdbcUsername, final String jdbcPassword) {
Map<String, String> dict = new HashMap<>();
Connection conn = null;
try {
Class.forName(jdbcClass);
conn = DriverManager.getConnection(
jdbcConn, jdbcUsername, jdbcPassword);
Statement stmt = conn.createStatement();
ResultSet rs = stmt.executeQuery("select dim1, dim1_val from t_dim1;");
while(rs.next()) {
dict.put(rs.getString(1), rs.getString(2));
}
dict.forEach((k, v) ->
logger.debug("bindiego from mysql: key = " + k + " value = " + v));
} catch (Exception ex) {
logger.error(ex.getMessage(), ex);
} finally {
// if (conn != null)
try {
conn.close();
} catch (Exception ex) {
logger.warn(ex.getMessage(), ex);
}
}
/*
Instant now = Instant.now();
org.joda.time.format.DateTimeFormatter dtf =
org.joda.time.format.DateTimeFormat.forPattern("HH:MM:SS");
dict.put("bindiego_A", now.minus(Duration.standardSeconds(30)).toString(dtf));
dict.put("bindiego_B", now.minus(Duration.standardSeconds(30)).toString());
*/
return dict;
}
}
/**
* Produce KV from the process CSV data for later 'aggregation by key' operations
*
* @Input processed csv data
* @Output <key, value>
* key = dim1, dim1 extraced from the processed csv data
* value = process csv data
*/
public static class ProduceKv extends DoFn<String, KV<String, String>> {
public ProduceKv(String delimiter) {
this.delimiter = delimiter;
}
@ProcessElement
public void processElement(ProcessContext ctx) throws IllegalArgumentException {
String[] csvData = ctx.element().split(delimiter);
//REVISIT: handle potential errors
ctx.output(KV.of(csvData[4], ctx.element()));
}
private String delimiter;
}
static void run(BindiegoStreamingOptions options) throws Exception {
// FileSystems.setDefaultPipelineOptions(options);
Pipeline p = Pipeline.create(options);
// Create a side input as a lookup table in order to enrich the input data
// Long is NOT infinite, but should be fine mostly :-)
/* REVISIT: This does NOT work, when updated, consumer's corresponding window will
* receive another view, so asSingleton() will not work. Multimap may double the RAM
* usage which is not a viable solution IMO. I would recommend wait for retraction or
* simply update the pipeline.
*/
/*
PCollectionView<Map<String, String>> lookupTable =
p.apply("Trigger for updating the side input (lookup table)",
GenerateSequence.from(0).withRate(1, Duration.standardSeconds(20L)))// FIXME: hardcoded
.apply(
Window.<Long>into(new GlobalWindows())
.triggering(Repeatedly.forever(AfterProcessingTime.pastFirstElementInPane()))
.discardingFiredPanes())
// .accumulatingAndRetractingFiredPanes())
.apply(
ParDo.of(
new DoFn<Long, Map<String, String>>() {
@ProcessElement
public void process(
@Element Long input,
PipelineOptions po,
OutputReceiver<Map<String, String>> o) {
BindiegoStreamingOptions op = po.as(BindiegoStreamingOptions.class);
o.output(BindiegoJdbcServiceExternal.read(
op.getJdbcClass(),
op.getJdbcConn(),
op.getJdbcUsername(),
op.getJdbcPassword()
));
}
})
)
.apply(Latest.<Map<String, String>>globally())
.apply(View.asSingleton());
*/
/*
* Debug code for JDBC data refresh
*/
/*
p.apply(GenerateSequence.from(0).withRate(1, Duration.standardSeconds(2L)))
.apply(Window.into(FixedWindows.of(Duration.standardSeconds(10))))
.apply(Min.longsGlobally().withoutDefaults())
.apply(
ParDo.of(
new DoFn<Long, KV<Long, Long>>() {
@ProcessElement
public void process(ProcessContext c) {
Map<String, String> kv = c.sideInput(lookupTable);
c.outputWithTimestamp(KV.of(1L, c.element()), Instant.now());
logger.debug("bindiego consumer - lookup table size: " + kv.size());
kv.forEach((k, v) ->
logger.debug("bindiego cnsumer - Key = " + k + ", Value = " + v));
}
}
).withSideInputs(lookupTable)
);
*/
/* Raw data processing */
PCollection<PubsubMessage> messages = p.apply("Read Pubsub Events",
PubsubIO
.readMessages().fromSubscription(options.getSubscription()));
/*
.readMessagesWithAttributesAndMessageId()
.withIdAttribute(options.getMessageIdAttr())
// set event time from a message attribute, milliseconds since the Unix epoch
.withTimestampAttribute(options.getMessageTsAttr())
.fromSubscription(options.getSubscription()));
*/
if (!options.getIsBasic()) {
// Read JDBC lookup table
final PCollectionView<Map<String, String>> lookupTable =
p.apply("Read lookup table from JDBC data source",
JdbcIO.<KV<String, String>>read()
// .withDataSourceConfiguration(
.withDataSourceProviderFn(JdbcIO.PoolableDataSourceProvider.of(
JdbcIO.DataSourceConfiguration.create(
options.getJdbcClass(), options.getJdbcConn())
.withUsername(options.getJdbcUsername())
.withPassword(options.getJdbcPassword())))
.withQuery("select dim1, dim1_val from t_dim1;") // FIXME: hardcoded
.withCoder(KvCoder.of(StringUtf8Coder.of(),
StringUtf8Coder.of())) // e.g. BigEndianIntegerCoder.of() for Integer
.withRowMapper(new JdbcIO.RowMapper<KV<String, String>>() {
public KV<String, String> mapRow(ResultSet resultSet) throws Exception {
return KV.of(resultSet.getString(1), resultSet.getString(2)); // e.g. resultSet.getInt(1)
}
})
)
.apply("Produce broadcast view for lookup", View.<String, String>asMap());
// Extact CSV data and join with lookup table
PCollectionTuple processedData = messages.apply("Extract CSV payload from pubsub message",
ParDo.of(new ExtractPayload(lookupTable))
.withOutputTags(STR_OUT, TupleTagList.of(STR_FAILURE_OUT))
.withSideInputs(lookupTable));
/* REVISIT: A terse approach */
PCollection<String> healthData = processedData.get(STR_OUT)
.apply(options.getWindowSize() + " window for healthy data",
Window.<String>into(FixedWindows.of(DurationUtils.parseDuration(options.getWindowSize())))
.triggering(
AfterWatermark.pastEndOfWindow()
.withEarlyFirings(
AfterProcessingTime
.pastFirstElementInPane()
.plusDelayOf(DurationUtils.parseDuration(options.getEarlyFiringPeriod())))
.withLateFirings(
AfterPane.elementCountAtLeast(
options.getLateFiringCount().intValue()))
)
.discardingFiredPanes() // e.g. .accumulatingFiredPanes() etc.
.withAllowedLateness(DurationUtils.parseDuration(options.getAllowedLateness()),
ClosingBehavior.FIRE_IF_NON_EMPTY))
.apply("Append windowing information",
ParDo.of(new AppendWindowInfo()));
// REVISIT: we may apply differnet window for error data?
PCollection<String> errData = processedData.get(STR_FAILURE_OUT)
.apply(options.getWindowSize() + " window for error data",
Window.<String>into(FixedWindows.of(DurationUtils.parseDuration(options.getWindowSize()))));
/* START - building realtime analytics */
// we use dim1 as key to do the analysis
// REVISIT: we applied the same windowing functions here, it could/should be different tho
// window/panes accumulating mode, good for tall table
processedData.get(STR_OUT)
.apply(options.getWindowSize()
+ " window for healthy data in KV for real time analysis, accumulating mode",
Window.<String>into(FixedWindows.of(DurationUtils.parseDuration(options.getWindowSize())))
.triggering(
AfterWatermark.pastEndOfWindow()
.withEarlyFirings(
AfterProcessingTime
.pastFirstElementInPane()
.plusDelayOf(DurationUtils.parseDuration(options.getEarlyFiringPeriod())))
.withLateFirings(
AfterPane.elementCountAtLeast(
options.getLateFiringCount().intValue()))
)
.accumulatingFiredPanes()
.withAllowedLateness(DurationUtils.parseDuration(options.getAllowedLateness()),
ClosingBehavior.FIRE_IF_NON_EMPTY))
.apply("Produce KV for aggregation operations", // produce PCollection<KV<String, String>>
ParDo.of(new ProduceKv(options.getCsvDelimiter())))
.apply("group by dim1 for analysis", // produce PCollection<KV<String, Iterable<String>>>
GroupByKey.create())
.apply("Produce HBase/Bigtable tall table",
ParDo.of(new DoFn<KV<String, Iterable<String>>, Put>() {
@ProcessElement
public void processElement(ProcessContext ctx) {
final long processTs = System.currentTimeMillis();
// row key related value
StringBuilder sb = new StringBuilder(ctx.element().getKey());
// statistical data, choose the appropreate data types according to your case
final byte[] stats_cf = Bytes.toBytes("stats");
Integer count = 0;
Integer sum = 0;
Integer min = Integer.MAX_VALUE;
Integer max = Integer.MIN_VALUE;
Float avg = 0F;
Iterable<String> csvLines = ctx.element().getValue();
// i.e. "event_ts,thread_id,thread_name,seq,dim1,metrics1,process_ts,dim1_val"
for(String csvLine : csvLines) {
String[] csvValues = csvLine.split(",");
Integer metric = Integer.valueOf(csvValues[5]);
++count;
sum += metric;
if (min > metric)
min = metric;
if (max < metric)
max = metric;
}
if (count > 0)
avg = sum.floatValue() / count;
ctx.output(
new Put(
Bytes.toBytes(sb.append('#')
.append(Long.MAX_VALUE - processTs).toString())
).addColumn(stats_cf, Bytes.toBytes("num_records"), processTs,
Bytes.toBytes(count.toString()))
.addColumn(stats_cf, Bytes.toBytes("sum"), processTs,
Bytes.toBytes(sum.toString()))
.addColumn(stats_cf, Bytes.toBytes("max"), processTs,
Bytes.toBytes(max.toString()))
.addColumn(stats_cf, Bytes.toBytes("min"), processTs,
Bytes.toBytes(min.toString()))
.addColumn(stats_cf, Bytes.toBytes("avg"), processTs,
Bytes.toBytes(avg.toString()))
);
}}))
.apply("Append window information",
ParDo.of(new DoFn<Put, Mutation>() {
@ProcessElement
public void processElement(ProcessContext ctx, IntervalWindow window)
throws IllegalArgumentException {
final byte[] win_cf = Bytes.toBytes("window_info");
Put p = ctx.element();
p.addColumn(win_cf, Bytes.toBytes("window"),
Bytes.toBytes(window.toString()))
.addColumn(win_cf, Bytes.toBytes("pane"),
Bytes.toBytes(ctx.pane().toString()))
.addColumn(win_cf, Bytes.toBytes("pane_idx"),
Bytes.toBytes(String.valueOf(ctx.pane().getIndex())))
.addColumn(win_cf, Bytes.toBytes("pane_nonspeculative_idx"),
Bytes.toBytes(String.valueOf(ctx.pane().getNonSpeculativeIndex())))
.addColumn(win_cf, Bytes.toBytes("is_first"),
Bytes.toBytes(String.valueOf(ctx.pane().isFirst())))
.addColumn(win_cf, Bytes.toBytes("is_last"),
Bytes.toBytes(String.valueOf(ctx.pane().isLast())))
.addColumn(win_cf, Bytes.toBytes("timing"),
Bytes.toBytes(ctx.pane().getTiming().toString()))
.addColumn(win_cf, Bytes.toBytes("win_start_ts"),
Bytes.toBytes(String.valueOf(window.start().getMillis())))
.addColumn(win_cf, Bytes.toBytes("win_end_ts"),
Bytes.toBytes(String.valueOf(window.end().getMillis())));
ctx.output(p);
}}))
.apply("Insert into Bigtable, tall schema",
CloudBigtableIO.writeToTable(
new CloudBigtableTableConfiguration.Builder()
.withProjectId(options.getProject())
.withInstanceId(options.getBtInstanceId())
.withTableId(options.getBtTableIdTall())
.build()));
/*
*/
// window/panes disgarding mode, good for wide table
processedData.get(STR_OUT)
.apply(options.getWindowSize()
+ " window for healthy data in KV for real time analysis, disgarding mode",
Window.<String>into(FixedWindows.of(DurationUtils.parseDuration(options.getWindowSize())))
.triggering(
AfterWatermark.pastEndOfWindow()
.withEarlyFirings(
AfterProcessingTime
.pastFirstElementInPane()
.plusDelayOf(DurationUtils.parseDuration(options.getEarlyFiringPeriod())))
.withLateFirings(
AfterPane.elementCountAtLeast(
options.getLateFiringCount().intValue()))
)
.discardingFiredPanes()
.withAllowedLateness(DurationUtils.parseDuration(options.getAllowedLateness()),
ClosingBehavior.FIRE_IF_NON_EMPTY))
.apply("Produce KV for aggregation operations", // produce PCollection<KV<String, String>>
ParDo.of(new ProduceKv(options.getCsvDelimiter())))
.apply("group by dim1 for analysis", // produce PCollection<KV<String, Iterable<String>>>
GroupByKey.create())
.apply("Produce HBase/Bigtable wide table, window/pane info append to column names",
ParDo.of(new DoFn<KV<String, Iterable<String>>, Mutation>() {
@ProcessElement
public void processElement(ProcessContext ctx, IntervalWindow window)
throws IllegalArgumentException {
final long processTs = System.currentTimeMillis();
// row key related value
StringBuilder sb = new StringBuilder(ctx.element().getKey());
// statistical data, choose the appropreate data types according to your case
final byte[] stats_cf = Bytes.toBytes("stats");
Integer count = 0;
Integer sum = 0;
Integer min = Integer.MAX_VALUE;
Integer max = Integer.MIN_VALUE;
Float avg = 0F;
Iterable<String> csvLines = ctx.element().getValue();
// i.e. "event_ts,thread_id,thread_name,seq,dim1,metrics1,process_ts,dim1_val"
for(String csvLine : csvLines) {
String[] csvValues = csvLine.split(",");
Integer metric = Integer.valueOf(csvValues[5]);
++count;
sum += metric;
if (min > metric)
min = metric;
if (max < metric)
max = metric;
}
if (count > 0)
avg = sum.floatValue() / count;
String pane_idx_str = String.valueOf(ctx.pane().getIndex());
ctx.output(
new Put(
Bytes.toBytes(sb.append('#')
.append(String.valueOf(window.start().getMillis()))
.append('#')
.append(String.valueOf(window.end().getMillis())).toString())
).addColumn(stats_cf, Bytes.toBytes("num_records#" + pane_idx_str),
processTs,
Bytes.toBytes(count.toString()))
.addColumn(stats_cf, Bytes.toBytes("sum#" + pane_idx_str),
processTs,
Bytes.toBytes(sum.toString()))
.addColumn(stats_cf, Bytes.toBytes("max#" + pane_idx_str),
processTs,
Bytes.toBytes(max.toString()))
.addColumn(stats_cf, Bytes.toBytes("min#" + pane_idx_str),
processTs,
Bytes.toBytes(min.toString()))
.addColumn(stats_cf, Bytes.toBytes("avg#" + pane_idx_str),
processTs,
Bytes.toBytes(avg.toString()))
);
}}))
.apply("Insert into Bigtable, wide schema",
CloudBigtableIO.writeToTable(
new CloudBigtableTableConfiguration.Builder()
.withProjectId(options.getProject())
.withInstanceId(options.getBtInstanceId())
.withTableId(options.getBtTableIdWide())
.build()));
/* END - building realtime analytics */
/* Elasticsearch */
processedData.get(STR_OUT)
.apply(options.getWindowSize() + " window for healthy data",
Window.<String>into(FixedWindows.of(DurationUtils.parseDuration(options.getWindowSize())))
.triggering(
AfterWatermark.pastEndOfWindow()
.withEarlyFirings(
AfterProcessingTime
.pastFirstElementInPane()
.plusDelayOf(DurationUtils.parseDuration(options.getEarlyFiringPeriod())))
.withLateFirings(
AfterPane.elementCountAtLeast(
options.getLateFiringCount().intValue()))
)
.discardingFiredPanes() // e.g. .accumulatingFiredPanes() etc.
.withAllowedLateness(DurationUtils.parseDuration(options.getAllowedLateness()),
ClosingBehavior.FIRE_IF_NON_EMPTY))
.apply("Prepare Elasticsearch Json data",
ParDo.of(new DoFn<String, String>() {
private ObjectMapper mapper;
@Setup
public void setup() {
mapper = new ObjectMapper();
}
@ProcessElement
public void processElement(ProcessContext ctx) throws Exception {
// "event_ts,thread_id,thread_name,seq,dim1,metrics1,process_ts,dim1_val"
String[] csvData = ctx.element().split(",");
Map<String, Object> jsonMap = new HashMap<>();
// FIXME: hardcoded
for (int i = 0; i < csvData.length; ++i) {
switch(i) {
case 0:
// jsonMap.put("@timestamp", new Date(Long.parseLong(csvData[i])));
jsonMap.put("@timestamp",
new java.sql.Timestamp(Long.parseLong(csvData[i])));
break;
case 1:
jsonMap.put("thread_id", csvData[i]);
break;
case 2:
jsonMap.put("thread_name", csvData[i]);
break;
case 3:
jsonMap.put("seq", Long.valueOf(csvData[i]));
break;
case 4:
jsonMap.put("dim1", csvData[i]);
break;
case 5:
jsonMap.put("metrics1", Long.valueOf(csvData[i]));
break;
case 6:
// jsonMap.put("process_ts", new Date(Long.parseLong(csvData[i])));
jsonMap.put("process_ts",
new java.sql.Timestamp(Long.parseLong(csvData[i])));
break;
case 7:
jsonMap.put("dim1_val", csvData[i]);
break;
default:
}
}
String json = mapper.writeValueAsString(jsonMap);
ctx.output(json);
}
}))
.apply("Append data to Elasticsearch",
ElasticsearchIO.append()
.withMaxBatchSize(options.getEsMaxBatchSize())
.withMaxBatchSizeBytes(options.getEsMaxBatchBytes())
.withConnectionConf(
ElasticsearchIO.ConnectionConf.create(
options.getEsHost(),
options.getEsIndex())
.withUsername(options.getEsUser())
.withPassword(options.getEsPass())
.withNumThread(options.getEsNumThread())
.withIngnoreInsecureSSL(options.getEsIsIgnoreInsecureSSL().booleanValue()))
//.withTrustSelfSignedCerts(true)) // false by default
.withRetryConf(
ElasticsearchIO.RetryConf.create(6, Duration.standardSeconds(60))));
/* END - Elasticsearch */
healthData.apply("Write windowed healthy CSV files",
TextIO.write()
.withNumShards(options.getNumShards())
.withWindowedWrites()
.to(
new WindowedFilenamePolicy(
options.getOutputDir(),
options.getFilenamePrefix(),
options.getOutputShardTemplate(),
options.getCsvFilenameSuffix()
))
.withTempDirectory(
FileBasedSink.convertToFileResourceIfPossible(options.getTempLocation())));
healthData.apply("Prepare table data for BigQuery",
ParDo.of(
new DoFn<String, TableRow>() {
@ProcessElement
public void processElement(ProcessContext ctx) {
String dataStr = ctx.element();
// REVISIT: damn ugly here, hard coded table schema
String[] cols = (
"event_ts,thread_id,thread_name,seq,dim1,metrics1"
+ ",process_ts,dim1_val"
+ ",window,pane_info,pane_idx,pane_nonspeculative_idx"
+ ",is_first,is_last,pane_timing,pane_event_ts"
).split(",");
// REFISIT: options is NOT serializable, make a class for this transform
// String[] csvData = dataStr.split(options.getCsvDelimiter());
String[] csvData = dataStr.split(",");
TableRow row = new TableRow();
// for god sake safety purpose
int loopCtr =
cols.length <= csvData.length ? cols.length : csvData.length;
for (int i = 0; i < loopCtr; ++i) {
// deal with non-string field in BQ
switch (i) {
case 0: case 6:
case 15:
row.set(cols[i],
TimeUnit.MILLISECONDS.toSeconds(
Long.parseLong(csvData[i])));
// row.set(cols[i], Long.parseLong(csvData[i]));
// row.set(cols[i], Long.parseLong(csvData[i])/1000);
// row.set(cols[i], Integer.parseInt(csvData[i]));
break;
case 10: case 11:
row.set(cols[i], Long.parseLong(csvData[i]));
break;
case 3: case 5:
row.set(cols[i], Integer.parseInt(csvData[i]));
break;
case 12: case 13:
row.set(cols[i], Boolean.parseBoolean(csvData[i]));
break;
default:
row.set(cols[i], csvData[i]);
}
} // End of dirty code
ctx.output(row);
}
}
))
.apply("Insert into BigQuery",
BigQueryIO.writeTableRows()
.withSchema(
NestedValueProvider.of(
options.getBqSchema(),
new SerializableFunction<String, TableSchema>() {
@Override
public TableSchema apply(String jsonPath) {
TableSchema tableSchema = new TableSchema();
List<TableFieldSchema> fields = new ArrayList<>();
SchemaParser schemaParser = new SchemaParser();
JSONObject jsonSchema;
try {
jsonSchema = schemaParser.parseSchema(jsonPath);
JSONArray bqSchemaJsonArray =
jsonSchema.getJSONArray(BIGQUERY_SCHEMA);
for (int i = 0; i < bqSchemaJsonArray.length(); i++) {
JSONObject inputField = bqSchemaJsonArray.getJSONObject(i);
TableFieldSchema field =
new TableFieldSchema()
.setName(inputField.getString(NAME))
.setType(inputField.getString(TYPE));
if (inputField.has(MODE)) {
field.setMode(inputField.getString(MODE));
}
fields.add(field);
}
tableSchema.setFields(fields);
} catch (Exception e) {
throw new RuntimeException(e);
}
return tableSchema;
}
}))
.withTimePartitioning(
new TimePartitioning().setField("event_ts")
.setType("DAY")
.setExpirationMs(null)
)
.withCreateDisposition(CreateDisposition.CREATE_IF_NEEDED)
.withWriteDisposition(WriteDisposition.WRITE_APPEND)
.to(options.getBqOutputTable())
.withMethod(BigQueryIO.Write.Method.STREAMING_INSERTS)
.withFailedInsertRetryPolicy(InsertRetryPolicy.retryTransientErrors())
.withCustomGcsTempLocation(options.getGcsTempLocation()));
// Assume dealing with CSV payload, so basically convert CSV to Avro
SchemaParser schemaParser = new SchemaParser();
String avroSchemaJson = schemaParser.getAvroSchema(options.getAvroSchema().get());
Schema avroSchema = new Schema.Parser().parse(avroSchemaJson);
healthData.apply("Prepare Avro data",
ParDo.of(new ConvertCsvToAvro(avroSchemaJson, options.getCsvDelimiter())))
.setCoder(AvroCoder.of(GenericRecord.class, avroSchema))
// .apply("Write Avro formatted data", AvroIO.writeGenericRecords(avroSchemaJson)
.apply("Write Avro formatted data", AvroIO.writeGenericRecords(avroSchema)
.to(
new WindowedFilenamePolicy(
options.getOutputDir(),
options.getFilenamePrefix(),
options.getOutputShardTemplate(),
options.getAvroFilenameSuffix()
))
.withWindowedWrites()
.withNumShards(options.getNumShards())
.withTempDirectory(
FileBasedSink.convertToFileResourceIfPossible(options.getTempLocation())
)
.withCodec(CodecFactory.snappyCodec()));
/*
.withTempDirectory(NestedValueProvider.of(
options.getGcsTempLocation(),
(SerializableFunction<String, ResourceId>) input ->
FileBasedSink.convertToFileResourceIfPossible(input)
))
*/
errData.apply("Write windowed error data in CSV format",
TextIO.write()
.withNumShards(options.getNumShards())
.withWindowedWrites()
.to(
new WindowedFilenamePolicy(
options.getErrOutputDir(),
options.getFilenamePrefix(),
options.getOutputShardTemplate(),
options.getCsvFilenameSuffix()
))
.withTempDirectory(
FileBasedSink.convertToFileResourceIfPossible(options.getTempLocation())));
p.run();
//p.run().waitUntilFinish();
} else { // end if BasicMode
PCollectionTuple strData = messages.apply("Extract pubsub messages",
ParDo.of(new DoFn<PubsubMessage,String>() {
@ProcessElement
public void processElement(ProcessContext ctx, MultiOutputReceiver r)
throws IllegalArgumentException {
String payload = null; // Firebase Json data
try {
PubsubMessage psmsg = ctx.element();
payload = new String(psmsg.getPayload(), StandardCharsets.UTF_8);
logger.debug("Extracted payload: " + payload);
r.get(STR_OUT).output(payload);
} catch (Exception ex) {
if (null == payload)
payload = "Failed to extract pubsub payload";
r.get(STR_FAILURE_OUT).output(payload);
logger.error("Failed extract pubsub message", ex);
}
}
}).withOutputTags(STR_OUT, TupleTagList.of(STR_FAILURE_OUT))
);
PCollection<TableRow> bqData = strData.get(STR_OUT)
/*
.apply(options.getWindowSize() + " window for bounded data",
Window.<String>into(FixedWindows.of(DurationUtils.parseDuration(options.getWindowSize())))
.triggering(
AfterWatermark.pastEndOfWindow()
.withEarlyFirings(
AfterProcessingTime
.pastFirstElementInPane()
.plusDelayOf(DurationUtils.parseDuration(options.getEarlyFiringPeriod())))
.withLateFirings(
AfterPane.elementCountAtLeast(
options.getLateFiringCount().intValue()))
)
.discardingFiredPanes() // e.g. .accumulatingFiredPanes() etc.
.withAllowedLateness(DurationUtils.parseDuration(options.getAllowedLateness()),
ClosingBehavior.FIRE_IF_NON_EMPTY))
*/