use of com.ibm.cohort.cql.spark.metadata.EvaluationSummary in project quality-measure-and-cohort-service by Alvearie.
the class SparkCqlEvaluatorTest method checkEvaluationSummaryFieldsPopulated.
private void checkEvaluationSummaryFieldsPopulated(Path summaryPath, int totalContexts, boolean hasErrors) throws IOException {
try (FileInputStream fileInputStream = new FileInputStream(summaryPath.toFile())) {
ObjectMapper mapper = new ObjectMapper();
EvaluationSummary evaluationSummary = mapper.readValue(fileInputStream, EvaluationSummary.class);
assertNotNull(evaluationSummary.getApplicationId());
assertNotNull(evaluationSummary.getCorrelationId());
assertTrue(evaluationSummary.getStartTimeMillis() > 0);
assertTrue(evaluationSummary.getEndTimeMillis() > 0);
assertTrue(evaluationSummary.getRuntimeMillis() > 0);
assertEquals(totalContexts, evaluationSummary.getTotalContexts());
assertEquals(totalContexts, evaluationSummary.getExecutionsPerContext().size());
assertEquals(totalContexts, evaluationSummary.getRuntimeMillisPerContext().size());
if (hasErrors) {
assertTrue(CollectionUtils.isNotEmpty(evaluationSummary.getErrorList()));
assertTrue(evaluationSummary.getJobStatus() == JobStatus.FAIL);
} else {
assertTrue(CollectionUtils.isEmpty(evaluationSummary.getErrorList()));
assertTrue(evaluationSummary.getJobStatus() == JobStatus.SUCCESS);
}
}
}
use of com.ibm.cohort.cql.spark.metadata.EvaluationSummary in project quality-measure-and-cohort-service by Alvearie.
the class SparkCqlEvaluator method run.
public void run(PrintStream out) throws Exception {
EvaluationSummary evaluationSummary = new EvaluationSummary();
long startTimeMillis = System.currentTimeMillis();
evaluationSummary.setStartTimeMillis(startTimeMillis);
evaluationSummary.setJobStatus(JobStatus.FAIL);
SparkSession.Builder sparkBuilder = SparkSession.builder();
try (SparkSession spark = sparkBuilder.getOrCreate()) {
final LongAccumulator contextAccum = spark.sparkContext().longAccumulator("Context");
final CollectionAccumulator<EvaluationError> errorAccumulator = spark.sparkContext().collectionAccumulator("EvaluationErrors");
try {
spark.sparkContext().setLocalProperty("mdc." + CORRELATION_ID, MDC.get(CORRELATION_ID));
evaluationSummary.setCorrelationId(MDC.get(CORRELATION_ID));
boolean useJava8API = Boolean.valueOf(spark.conf().get("spark.sql.datetime.java8API.enabled"));
this.typeConverter = new SparkTypeConverter(useJava8API);
this.hadoopConfiguration = new SerializableConfiguration(spark.sparkContext().hadoopConfiguration());
evaluationSummary.setApplicationId(spark.sparkContext().applicationId());
CqlToElmTranslator cqlTranslator = getCqlTranslator();
SparkOutputColumnEncoder columnEncoder = getSparkOutputColumnEncoder();
ContextDefinitions contexts = readContextDefinitions(args.contextDefinitionPath);
List<ContextDefinition> filteredContexts = contexts.getContextDefinitions();
if (args.aggregationContexts != null && !args.aggregationContexts.isEmpty()) {
filteredContexts = filteredContexts.stream().filter(def -> args.aggregationContexts.contains(def.getName())).collect(Collectors.toList());
}
if (filteredContexts.isEmpty()) {
throw new IllegalArgumentException("At least one context definition is required (after filtering if enabled).");
}
Map<String, StructType> resultSchemas = calculateSparkSchema(filteredContexts.stream().map(ContextDefinition::getName).collect(Collectors.toList()), contexts, columnEncoder, cqlTranslator);
ZonedDateTime batchRunTime = ZonedDateTime.now();
final LongAccumulator perContextAccum = spark.sparkContext().longAccumulator("PerContext");
CustomMetricSparkPlugin.contextAccumGauge.setAccumulator(contextAccum);
CustomMetricSparkPlugin.perContextAccumGauge.setAccumulator(perContextAccum);
CustomMetricSparkPlugin.totalContextsToProcessCounter.inc(filteredContexts.size());
CustomMetricSparkPlugin.currentlyEvaluatingContextGauge.setValue(0);
ColumnRuleCreator columnRuleCreator = new ColumnRuleCreator(getFilteredJobSpecificationWithIds().getEvaluations(), getCqlTranslator(), createLibraryProvider());
Map<String, String> dataTypeAliases = createDataTypeAliases(filteredContexts, cqlTranslator);
for (ContextDefinition context : filteredContexts) {
final String contextName = context.getName();
ContextRetriever contextRetriever = new ContextRetriever(args.inputPaths, new DefaultDatasetRetriever(spark, args.inputFormat), args.disableColumnFiltering ? null : columnRuleCreator.getDataRequirementsForContext(context));
StructType resultsSchema = resultSchemas.get(contextName);
if (resultsSchema == null || resultsSchema.fields().length == 0) {
LOG.warn("Context " + contextName + " has no defines configured. Skipping.");
} else {
LOG.info("Evaluating context " + contextName);
long contextStartMillis = System.currentTimeMillis();
final String outputPath = MapUtils.getRequiredKey(args.outputPaths, context.getName(), "outputPath");
JavaPairRDD<Object, List<Row>> rowsByContextId = contextRetriever.retrieveContext(context);
CustomMetricSparkPlugin.currentlyEvaluatingContextGauge.setValue(CustomMetricSparkPlugin.currentlyEvaluatingContextGauge.getValue() + 1);
JavaPairRDD<Object, Row> resultsByContext = rowsByContextId.flatMapToPair(x -> evaluate(contextName, resultsSchema, x, dataTypeAliases, perContextAccum, errorAccumulator, batchRunTime));
writeResults(spark, resultsSchema, resultsByContext, outputPath);
long contextEndMillis = System.currentTimeMillis();
LOG.info(String.format("Wrote results for context %s to %s", contextName, outputPath));
evaluationSummary.addContextCount(contextName, perContextAccum.value());
evaluationSummary.addContextRuntime(contextName, contextEndMillis - contextStartMillis);
contextAccum.add(1);
perContextAccum.reset();
}
}
CustomMetricSparkPlugin.currentlyEvaluatingContextGauge.setValue(0);
try {
Boolean metricsEnabledStr = Boolean.valueOf(spark.conf().get("spark.ui.prometheus.enabled"));
if (metricsEnabledStr) {
LOG.info("Prometheus metrics enabled, sleeping for 7 seconds to finish gathering metrics");
// sleep for over 5 seconds because Prometheus only polls
// every 5 seconds. If spark finishes and goes away immediately after completing,
// Prometheus will never be able to poll for the final set of metrics for the spark-submit
// The default promtheus config map was changed from 2 minute scrape interval to 5 seconds for spark pods
Thread.sleep(7000);
} else {
LOG.info("Prometheus metrics not enabled");
}
} catch (NoSuchElementException e) {
LOG.info("spark.ui.prometheus.enabled is not set");
}
evaluationSummary.setJobStatus(JobStatus.SUCCESS);
} catch (Exception e) {
// If we experience an error that would make the program halt, capture the error
// and report it in the batch summary file
ByteArrayOutputStream errorDetailStream = new ByteArrayOutputStream();
try (PrintStream printStream = new PrintStream(errorDetailStream)) {
printStream.write(e.getMessage().getBytes());
printStream.write('\n');
if (e.getCause() != null) {
printStream.write(e.getCause().getMessage().getBytes());
printStream.write('\n');
}
e.printStackTrace(printStream);
evaluationSummary.setErrorList(Collections.singletonList(new EvaluationError(null, null, null, errorDetailStream.toString())));
}
throw e;
} finally {
long endTimeMillis = System.currentTimeMillis();
evaluationSummary.setEndTimeMillis(endTimeMillis);
evaluationSummary.setRuntimeMillis(endTimeMillis - startTimeMillis);
if (args.metadataOutputPath != null) {
if (evaluationSummary.getErrorList() == null) {
evaluationSummary.setErrorList(errorAccumulator.value());
}
if (CollectionUtils.isNotEmpty(evaluationSummary.getErrorList())) {
evaluationSummary.setJobStatus(JobStatus.FAIL);
}
evaluationSummary.setTotalContexts(contextAccum.value());
OutputMetadataWriter writer = getOutputMetadataWriter();
writer.writeMetadata(evaluationSummary);
}
}
}
}
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