use of io.druid.query.Druids.SegmentMetadataQueryBuilder in project hive by apache.
the class DruidQueryBasedInputFormat method splitSelectQuery.
/* Method that splits Select query depending on the threshold so read can be
* parallelized. We will only contact the Druid broker to obtain all results. */
private static HiveDruidSplit[] splitSelectQuery(Configuration conf, String address, SelectQuery query, Path dummyPath) throws IOException {
final int selectThreshold = (int) HiveConf.getIntVar(conf, HiveConf.ConfVars.HIVE_DRUID_SELECT_THRESHOLD);
final int numConnection = HiveConf.getIntVar(conf, HiveConf.ConfVars.HIVE_DRUID_NUM_HTTP_CONNECTION);
final Period readTimeout = new Period(HiveConf.getVar(conf, HiveConf.ConfVars.HIVE_DRUID_HTTP_READ_TIMEOUT));
final boolean isFetch = query.getContextBoolean(Constants.DRUID_QUERY_FETCH, false);
if (isFetch) {
// If it has a limit, we use it and we do not split the query
return new HiveDruidSplit[] { new HiveDruidSplit(DruidStorageHandlerUtils.JSON_MAPPER.writeValueAsString(query), dummyPath, new String[] { address }) };
}
// We do not have the number of rows, thus we need to execute a
// Segment Metadata query to obtain number of rows
SegmentMetadataQueryBuilder metadataBuilder = new Druids.SegmentMetadataQueryBuilder();
metadataBuilder.dataSource(query.getDataSource());
metadataBuilder.intervals(query.getIntervals());
metadataBuilder.merge(true);
metadataBuilder.analysisTypes();
SegmentMetadataQuery metadataQuery = metadataBuilder.build();
Lifecycle lifecycle = new Lifecycle();
HttpClient client = HttpClientInit.createClient(HttpClientConfig.builder().withNumConnections(numConnection).withReadTimeout(readTimeout.toStandardDuration()).build(), lifecycle);
try {
lifecycle.start();
} catch (Exception e) {
LOG.error("Lifecycle start issue");
throw new IOException(org.apache.hadoop.util.StringUtils.stringifyException(e));
}
InputStream response;
try {
response = DruidStorageHandlerUtils.submitRequest(client, DruidStorageHandlerUtils.createRequest(address, metadataQuery));
} catch (Exception e) {
lifecycle.stop();
throw new IOException(org.apache.hadoop.util.StringUtils.stringifyException(e));
}
// Retrieve results
List<SegmentAnalysis> metadataList;
try {
metadataList = DruidStorageHandlerUtils.SMILE_MAPPER.readValue(response, new TypeReference<List<SegmentAnalysis>>() {
});
} catch (Exception e) {
response.close();
throw new IOException(org.apache.hadoop.util.StringUtils.stringifyException(e));
} finally {
lifecycle.stop();
}
if (metadataList == null) {
throw new IOException("Connected to Druid but could not retrieve datasource information");
}
if (metadataList.isEmpty()) {
// There are no rows for that time range, we can submit query as it is
return new HiveDruidSplit[] { new HiveDruidSplit(DruidStorageHandlerUtils.JSON_MAPPER.writeValueAsString(query), dummyPath, new String[] { address }) };
}
if (metadataList.size() != 1) {
throw new IOException("Information about segments should have been merged");
}
final long numRows = metadataList.get(0).getNumRows();
query = query.withPagingSpec(PagingSpec.newSpec(Integer.MAX_VALUE));
if (numRows <= selectThreshold) {
// We are not going to split it
return new HiveDruidSplit[] { new HiveDruidSplit(DruidStorageHandlerUtils.JSON_MAPPER.writeValueAsString(query), dummyPath, new String[] { address }) };
}
// If the query does not specify a timestamp, we obtain the total time using
// a Time Boundary query. Then, we use the information to split the query
// following the Select threshold configuration property
final List<Interval> intervals = new ArrayList<>();
if (query.getIntervals().size() == 1 && query.getIntervals().get(0).withChronology(ISOChronology.getInstanceUTC()).equals(DruidTable.DEFAULT_INTERVAL)) {
// Default max and min, we should execute a time boundary query to get a
// more precise range
TimeBoundaryQueryBuilder timeBuilder = new Druids.TimeBoundaryQueryBuilder();
timeBuilder.dataSource(query.getDataSource());
TimeBoundaryQuery timeQuery = timeBuilder.build();
lifecycle = new Lifecycle();
client = HttpClientInit.createClient(HttpClientConfig.builder().withNumConnections(numConnection).withReadTimeout(readTimeout.toStandardDuration()).build(), lifecycle);
try {
lifecycle.start();
} catch (Exception e) {
LOG.error("Lifecycle start issue");
throw new IOException(org.apache.hadoop.util.StringUtils.stringifyException(e));
}
try {
response = DruidStorageHandlerUtils.submitRequest(client, DruidStorageHandlerUtils.createRequest(address, timeQuery));
} catch (Exception e) {
lifecycle.stop();
throw new IOException(org.apache.hadoop.util.StringUtils.stringifyException(e));
}
// Retrieve results
List<Result<TimeBoundaryResultValue>> timeList;
try {
timeList = DruidStorageHandlerUtils.SMILE_MAPPER.readValue(response, new TypeReference<List<Result<TimeBoundaryResultValue>>>() {
});
} catch (Exception e) {
response.close();
throw new IOException(org.apache.hadoop.util.StringUtils.stringifyException(e));
} finally {
lifecycle.stop();
}
if (timeList == null || timeList.isEmpty()) {
throw new IOException("Connected to Druid but could not retrieve time boundary information");
}
if (timeList.size() != 1) {
throw new IOException("We should obtain a single time boundary");
}
intervals.add(new Interval(timeList.get(0).getValue().getMinTime().getMillis(), timeList.get(0).getValue().getMaxTime().getMillis(), ISOChronology.getInstanceUTC()));
} else {
intervals.addAll(query.getIntervals());
}
// Create (numRows/default threshold) input splits
int numSplits = (int) Math.ceil((double) numRows / selectThreshold);
List<List<Interval>> newIntervals = createSplitsIntervals(intervals, numSplits);
HiveDruidSplit[] splits = new HiveDruidSplit[numSplits];
for (int i = 0; i < numSplits; i++) {
// Create partial Select query
final SelectQuery partialQuery = query.withQuerySegmentSpec(new MultipleIntervalSegmentSpec(newIntervals.get(i)));
splits[i] = new HiveDruidSplit(DruidStorageHandlerUtils.JSON_MAPPER.writeValueAsString(partialQuery), dummyPath, new String[] { address });
}
return splits;
}
use of io.druid.query.Druids.SegmentMetadataQueryBuilder in project hive by apache.
the class DruidSerDe method inferSchema.
/* Select query */
private void inferSchema(SelectQuery query, List<String> columnNames, List<PrimitiveTypeInfo> columnTypes, String address) throws SerDeException {
// Timestamp column
columnNames.add(DruidTable.DEFAULT_TIMESTAMP_COLUMN);
columnTypes.add(TypeInfoFactory.timestampTypeInfo);
// Dimension columns
for (DimensionSpec ds : query.getDimensions()) {
columnNames.add(ds.getOutputName());
columnTypes.add(TypeInfoFactory.stringTypeInfo);
}
// The type for metric columns is not explicit in the query, thus in this case
// we need to emit a metadata query to know their type
SegmentMetadataQueryBuilder builder = new Druids.SegmentMetadataQueryBuilder();
builder.dataSource(query.getDataSource());
builder.merge(true);
builder.analysisTypes();
SegmentMetadataQuery metadataQuery = builder.build();
// Execute query in Druid
SegmentAnalysis schemaInfo;
try {
schemaInfo = submitMetadataRequest(address, metadataQuery);
} catch (IOException e) {
throw new SerDeException(e);
}
if (schemaInfo == null) {
throw new SerDeException("Connected to Druid but could not retrieve datasource information");
}
for (String metric : query.getMetrics()) {
columnNames.add(metric);
columnTypes.add(DruidSerDeUtils.convertDruidToHiveType(schemaInfo.getColumns().get(metric).getType()));
}
}
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