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[HUDI-298] Fix issue with incorrect column mapping casusing bad data, during on-the-fly merge of Real Time tables (#956)

* Fix issue with incorrect column mapping casusing bad data, during on-the-fly merge of Real Time tables
This commit is contained in:
Udit Mehrotra
2019-10-16 02:05:53 -07:00
committed by Balaji Varadarajan
parent c052167c06
commit 12523c379f
6 changed files with 88 additions and 38 deletions

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@@ -27,6 +27,7 @@ import org.apache.avro.Schema;
import org.apache.avro.generic.GenericRecord; import org.apache.avro.generic.GenericRecord;
import org.apache.avro.generic.GenericRecordBuilder; import org.apache.avro.generic.GenericRecordBuilder;
import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hive.metastore.api.hive_metastoreConstants;
import org.apache.hadoop.hive.serde2.ColumnProjectionUtils; import org.apache.hadoop.hive.serde2.ColumnProjectionUtils;
import org.apache.hadoop.io.ArrayWritable; import org.apache.hadoop.io.ArrayWritable;
import org.apache.hadoop.io.Writable; import org.apache.hadoop.io.Writable;
@@ -82,12 +83,23 @@ public class HoodieMergeOnReadTestUtils {
String names = fields.stream().map(f -> f.name().toString()).collect(Collectors.joining(",")); String names = fields.stream().map(f -> f.name().toString()).collect(Collectors.joining(","));
String postions = fields.stream().map(f -> String.valueOf(f.pos())).collect(Collectors.joining(",")); String postions = fields.stream().map(f -> String.valueOf(f.pos())).collect(Collectors.joining(","));
Configuration conf = HoodieTestUtils.getDefaultHadoopConf(); Configuration conf = HoodieTestUtils.getDefaultHadoopConf();
String hiveColumnNames = fields.stream().filter(field -> !field.name().equalsIgnoreCase("datestr"))
.map(Schema.Field::name).collect(Collectors.joining(","));
hiveColumnNames = hiveColumnNames + ",datestr";
String hiveColumnTypes = HoodieAvroUtils.addMetadataColumnTypes(HoodieTestDataGenerator.TRIP_HIVE_COLUMN_TYPES);
hiveColumnTypes = hiveColumnTypes + ",string";
jobConf.set(hive_metastoreConstants.META_TABLE_COLUMNS, hiveColumnNames);
jobConf.set(hive_metastoreConstants.META_TABLE_COLUMN_TYPES, hiveColumnTypes);
jobConf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names); jobConf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names);
jobConf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, postions); jobConf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, postions);
jobConf.set("partition_columns", "datestr"); jobConf.set(hive_metastoreConstants.META_TABLE_PARTITION_COLUMNS, "datestr");
conf.set(hive_metastoreConstants.META_TABLE_COLUMNS, hiveColumnNames);
conf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names); conf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names);
conf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, postions); conf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, postions);
conf.set("partition_columns", "datestr"); conf.set(hive_metastoreConstants.META_TABLE_PARTITION_COLUMNS, "datestr");
conf.set(hive_metastoreConstants.META_TABLE_COLUMN_TYPES, hiveColumnTypes);
inputFormat.setConf(conf); inputFormat.setConf(conf);
jobConf.addResource(conf); jobConf.addResource(conf);
} }

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@@ -78,6 +78,7 @@ public class HoodieTestDataGenerator {
+ "{\"name\": \"begin_lat\", \"type\": \"double\"}," + "{\"name\": \"begin_lon\", \"type\": \"double\"}," + "{\"name\": \"begin_lat\", \"type\": \"double\"}," + "{\"name\": \"begin_lon\", \"type\": \"double\"},"
+ "{\"name\": \"end_lat\", \"type\": \"double\"}," + "{\"name\": \"end_lon\", \"type\": \"double\"}," + "{\"name\": \"end_lat\", \"type\": \"double\"}," + "{\"name\": \"end_lon\", \"type\": \"double\"},"
+ "{\"name\":\"fare\",\"type\": \"double\"}]}"; + "{\"name\":\"fare\",\"type\": \"double\"}]}";
public static String TRIP_HIVE_COLUMN_TYPES = "double,string,string,string,double,double,double,double,double";
public static Schema avroSchema = new Schema.Parser().parse(TRIP_EXAMPLE_SCHEMA); public static Schema avroSchema = new Schema.Parser().parse(TRIP_EXAMPLE_SCHEMA);
public static Schema avroSchemaWithMetadataFields = HoodieAvroUtils.addMetadataFields(avroSchema); public static Schema avroSchemaWithMetadataFields = HoodieAvroUtils.addMetadataFields(avroSchema);

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@@ -130,6 +130,10 @@ public class HoodieAvroUtils {
return mergedSchema; return mergedSchema;
} }
public static String addMetadataColumnTypes(String hiveColumnTypes) {
return "string,string,string,string,string," + hiveColumnTypes;
}
private static Schema initRecordKeySchema() { private static Schema initRecordKeySchema() {
Schema.Field recordKeyField = Schema.Field recordKeyField =
new Schema.Field(HoodieRecord.RECORD_KEY_METADATA_FIELD, METADATA_FIELD_SCHEMA, "", NullNode.getInstance()); new Schema.Field(HoodieRecord.RECORD_KEY_METADATA_FIELD, METADATA_FIELD_SCHEMA, "", NullNode.getInstance());

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@@ -33,6 +33,7 @@ import org.apache.avro.generic.GenericFixed;
import org.apache.avro.generic.GenericRecord; import org.apache.avro.generic.GenericRecord;
import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path; import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hive.metastore.api.hive_metastoreConstants;
import org.apache.hadoop.hive.serde2.ColumnProjectionUtils; import org.apache.hadoop.hive.serde2.ColumnProjectionUtils;
import org.apache.hadoop.hive.serde2.io.DoubleWritable; import org.apache.hadoop.hive.serde2.io.DoubleWritable;
import org.apache.hadoop.io.ArrayWritable; import org.apache.hadoop.io.ArrayWritable;
@@ -89,13 +90,14 @@ public abstract class AbstractRealtimeRecordReader {
// Schema handles // Schema handles
private Schema readerSchema; private Schema readerSchema;
private Schema writerSchema; private Schema writerSchema;
private Schema hiveSchema;
public AbstractRealtimeRecordReader(HoodieRealtimeFileSplit split, JobConf job) { public AbstractRealtimeRecordReader(HoodieRealtimeFileSplit split, JobConf job) {
this.split = split; this.split = split;
this.jobConf = job; this.jobConf = job;
LOG.info("cfg ==> " + job.get(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR)); LOG.info("cfg ==> " + job.get(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR));
LOG.info("columnIds ==> " + job.get(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR)); LOG.info("columnIds ==> " + job.get(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR));
LOG.info("partitioningColumns ==> " + job.get("partition_columns", "")); LOG.info("partitioningColumns ==> " + job.get(hive_metastoreConstants.META_TABLE_PARTITION_COLUMNS, ""));
try { try {
this.usesCustomPayload = usesCustomPayload(); this.usesCustomPayload = usesCustomPayload();
LOG.info("usesCustomPayload ==> " + this.usesCustomPayload); LOG.info("usesCustomPayload ==> " + this.usesCustomPayload);
@@ -179,7 +181,8 @@ public abstract class AbstractRealtimeRecordReader {
/** /**
* Generate a reader schema off the provided writeSchema, to just project out the provided columns * Generate a reader schema off the provided writeSchema, to just project out the provided columns
*/ */
public static Schema generateProjectionSchema(Schema writeSchema, List<String> fieldNames) { public static Schema generateProjectionSchema(Schema writeSchema, Map<String, Field> schemaFieldsMap,
List<String> fieldNames) {
/** /**
* Avro & Presto field names seems to be case sensitive (support fields differing only in case) whereas * Avro & Presto field names seems to be case sensitive (support fields differing only in case) whereas
* Hive/Impala/SparkSQL(default) are case-insensitive. Spark allows this to be configurable using * Hive/Impala/SparkSQL(default) are case-insensitive. Spark allows this to be configurable using
@@ -191,8 +194,6 @@ public abstract class AbstractRealtimeRecordReader {
* *
*/ */
List<Schema.Field> projectedFields = new ArrayList<>(); List<Schema.Field> projectedFields = new ArrayList<>();
Map<String, Schema.Field> schemaFieldsMap = writeSchema.getFields().stream()
.map(r -> Pair.of(r.name().toLowerCase(), r)).collect(Collectors.toMap(Pair::getLeft, Pair::getRight));
for (String fn : fieldNames) { for (String fn : fieldNames) {
Schema.Field field = schemaFieldsMap.get(fn.toLowerCase()); Schema.Field field = schemaFieldsMap.get(fn.toLowerCase());
if (field == null) { if (field == null) {
@@ -209,6 +210,11 @@ public abstract class AbstractRealtimeRecordReader {
return projectedSchema; return projectedSchema;
} }
public static Map<String, Field> getNameToFieldMap(Schema schema) {
return schema.getFields().stream().map(r -> Pair.of(r.name().toLowerCase(), r))
.collect(Collectors.toMap(Pair::getLeft, Pair::getRight));
}
/** /**
* Convert the projected read from delta record into an array writable * Convert the projected read from delta record into an array writable
*/ */
@@ -321,20 +327,48 @@ public abstract class AbstractRealtimeRecordReader {
LOG.debug("Writer Schema From Log => " + writerSchema.getFields()); LOG.debug("Writer Schema From Log => " + writerSchema.getFields());
} }
// Add partitioning fields to writer schema for resulting row to contain null values for these fields // Add partitioning fields to writer schema for resulting row to contain null values for these fields
String partitionFields = jobConf.get("partition_columns", ""); String partitionFields = jobConf.get(hive_metastoreConstants.META_TABLE_PARTITION_COLUMNS, "");
List<String> partitioningFields = List<String> partitioningFields =
partitionFields.length() > 0 ? Arrays.stream(partitionFields.split(",")).collect(Collectors.toList()) partitionFields.length() > 0 ? Arrays.stream(partitionFields.split(",")).collect(Collectors.toList())
: new ArrayList<>(); : new ArrayList<>();
writerSchema = addPartitionFields(writerSchema, partitioningFields); writerSchema = addPartitionFields(writerSchema, partitioningFields);
List<String> projectionFields = orderFields(jobConf.get(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR), List<String> projectionFields = orderFields(jobConf.get(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR),
jobConf.get(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR), partitioningFields); jobConf.get(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR), partitioningFields);
Map<String, Field> schemaFieldsMap = getNameToFieldMap(writerSchema);
hiveSchema = constructHiveOrderedSchema(writerSchema, schemaFieldsMap);
// TODO(vc): In the future, the reader schema should be updated based on log files & be able // TODO(vc): In the future, the reader schema should be updated based on log files & be able
// to null out fields not present before // to null out fields not present before
readerSchema = generateProjectionSchema(writerSchema, projectionFields);
readerSchema = generateProjectionSchema(writerSchema, schemaFieldsMap, projectionFields);
LOG.info(String.format("About to read compacted logs %s for base split %s, projecting cols %s", LOG.info(String.format("About to read compacted logs %s for base split %s, projecting cols %s",
split.getDeltaFilePaths(), split.getPath(), projectionFields)); split.getDeltaFilePaths(), split.getPath(), projectionFields));
} }
private Schema constructHiveOrderedSchema(Schema writerSchema, Map<String, Field> schemaFieldsMap) {
// Get all column names of hive table
String hiveColumnString = jobConf.get(hive_metastoreConstants.META_TABLE_COLUMNS);
String[] hiveColumns = hiveColumnString.split(",");
List<Field> hiveSchemaFields = new ArrayList<>();
for (String columnName : hiveColumns) {
Field field = schemaFieldsMap.get(columnName.toLowerCase());
if (field != null) {
hiveSchemaFields.add(new Schema.Field(field.name(), field.schema(), field.doc(), field.defaultValue()));
} else {
// Hive has some extra virtual columns like BLOCK__OFFSET__INSIDE__FILE which do not exist in table schema.
// They will get skipped as they won't be found in the original schema.
LOG.debug("Skipping Hive Column => " + columnName);
}
}
Schema hiveSchema = Schema.createRecord(writerSchema.getName(), writerSchema.getDoc(), writerSchema.getNamespace(),
writerSchema.isError());
hiveSchema.setFields(hiveSchemaFields);
return hiveSchema;
}
public Schema getReaderSchema() { public Schema getReaderSchema() {
return readerSchema; return readerSchema;
} }
@@ -343,6 +377,10 @@ public abstract class AbstractRealtimeRecordReader {
return writerSchema; return writerSchema;
} }
public Schema getHiveSchema() {
return hiveSchema;
}
public long getMaxCompactionMemoryInBytes() { public long getMaxCompactionMemoryInBytes() {
// jobConf.getMemoryForMapTask() returns in MB // jobConf.getMemoryForMapTask() returns in MB
return (long) Math return (long) Math

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@@ -101,7 +101,7 @@ class RealtimeCompactedRecordReader extends AbstractRealtimeRecordReader
} }
// we assume, a later safe record in the log, is newer than what we have in the map & // we assume, a later safe record in the log, is newer than what we have in the map &
// replace it. // replace it.
ArrayWritable aWritable = (ArrayWritable) avroToArrayWritable(recordToReturn, getWriterSchema()); ArrayWritable aWritable = (ArrayWritable) avroToArrayWritable(recordToReturn, getHiveSchema());
Writable[] replaceValue = aWritable.get(); Writable[] replaceValue = aWritable.get();
if (LOG.isDebugEnabled()) { if (LOG.isDebugEnabled()) {
LOG.debug(String.format("key %s, base values: %s, log values: %s", key, arrayWritableToString(arrayWritable), LOG.debug(String.format("key %s, base values: %s, log values: %s", key, arrayWritableToString(arrayWritable),

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@@ -36,6 +36,7 @@ import org.apache.avro.generic.IndexedRecord;
import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path; import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hive.metastore.api.hive_metastoreConstants;
import org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat; import org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat;
import org.apache.hadoop.hive.serde2.ColumnProjectionUtils; import org.apache.hadoop.hive.serde2.ColumnProjectionUtils;
import org.apache.hadoop.io.ArrayWritable; import org.apache.hadoop.io.ArrayWritable;
@@ -76,6 +77,8 @@ import org.junit.rules.TemporaryFolder;
public class HoodieRealtimeRecordReaderTest { public class HoodieRealtimeRecordReaderTest {
private static final String PARTITION_COLUMN = "datestr";
private JobConf jobConf; private JobConf jobConf;
private FileSystem fs; private FileSystem fs;
private Configuration hadoopConf; private Configuration hadoopConf;
@@ -158,7 +161,22 @@ public class HoodieRealtimeRecordReaderTest {
testReader(false); testReader(false);
} }
public void testReader(boolean partitioned) throws Exception { private void setHiveColumnNameProps(List<Schema.Field> fields, JobConf jobConf, boolean isPartitioned) {
String names = fields.stream().map(Field::name).collect(Collectors.joining(","));
String postions = fields.stream().map(f -> String.valueOf(f.pos())).collect(Collectors.joining(","));
jobConf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names);
jobConf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, postions);
String hiveOrderedColumnNames = fields.stream().filter(field -> !field.name().equalsIgnoreCase(PARTITION_COLUMN))
.map(Field::name).collect(Collectors.joining(","));
if (isPartitioned) {
hiveOrderedColumnNames += "," + PARTITION_COLUMN;
jobConf.set(hive_metastoreConstants.META_TABLE_PARTITION_COLUMNS, PARTITION_COLUMN);
}
jobConf.set(hive_metastoreConstants.META_TABLE_COLUMNS, hiveOrderedColumnNames);
}
private void testReader(boolean partitioned) throws Exception {
// initial commit // initial commit
Schema schema = HoodieAvroUtils.addMetadataFields(SchemaTestUtil.getEvolvedSchema()); Schema schema = HoodieAvroUtils.addMetadataFields(SchemaTestUtil.getEvolvedSchema());
HoodieTestUtils.init(hadoopConf, basePath.getRoot().getAbsolutePath(), HoodieTableType.MERGE_ON_READ); HoodieTestUtils.init(hadoopConf, basePath.getRoot().getAbsolutePath(), HoodieTableType.MERGE_ON_READ);
@@ -213,13 +231,7 @@ public class HoodieRealtimeRecordReaderTest {
new FileSplit(split.getPath(), 0, fs.getLength(split.getPath()), (String[]) null), jobConf, null); new FileSplit(split.getPath(), 0, fs.getLength(split.getPath()), (String[]) null), jobConf, null);
JobConf jobConf = new JobConf(); JobConf jobConf = new JobConf();
List<Schema.Field> fields = schema.getFields(); List<Schema.Field> fields = schema.getFields();
String names = fields.stream().map(f -> f.name().toString()).collect(Collectors.joining(",")); setHiveColumnNameProps(fields, jobConf, partitioned);
String postions = fields.stream().map(f -> String.valueOf(f.pos())).collect(Collectors.joining(","));
jobConf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names);
jobConf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, postions);
if (partitioned) {
jobConf.set("partition_columns", "datestr");
}
// validate record reader compaction // validate record reader compaction
HoodieRealtimeRecordReader recordReader = new HoodieRealtimeRecordReader(split, jobConf, reader); HoodieRealtimeRecordReader recordReader = new HoodieRealtimeRecordReader(split, jobConf, reader);
@@ -277,11 +289,7 @@ public class HoodieRealtimeRecordReaderTest {
new FileSplit(split.getPath(), 0, fs.getLength(split.getPath()), (String[]) null), jobConf, null); new FileSplit(split.getPath(), 0, fs.getLength(split.getPath()), (String[]) null), jobConf, null);
JobConf jobConf = new JobConf(); JobConf jobConf = new JobConf();
List<Schema.Field> fields = schema.getFields(); List<Schema.Field> fields = schema.getFields();
String names = fields.stream().map(f -> f.name().toString()).collect(Collectors.joining(",")); setHiveColumnNameProps(fields, jobConf, true);
String postions = fields.stream().map(f -> String.valueOf(f.pos())).collect(Collectors.joining(","));
jobConf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names);
jobConf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, postions);
jobConf.set("partition_columns", "datestr");
// Enable merge skipping. // Enable merge skipping.
jobConf.set("hoodie.realtime.merge.skip", "true"); jobConf.set("hoodie.realtime.merge.skip", "true");
@@ -356,12 +364,7 @@ public class HoodieRealtimeRecordReaderTest {
new FileSplit(split.getPath(), 0, fs.getLength(split.getPath()), (String[]) null), jobConf, null); new FileSplit(split.getPath(), 0, fs.getLength(split.getPath()), (String[]) null), jobConf, null);
JobConf jobConf = new JobConf(); JobConf jobConf = new JobConf();
List<Schema.Field> fields = schema.getFields(); List<Schema.Field> fields = schema.getFields();
setHiveColumnNameProps(fields, jobConf, true);
String names = fields.stream().map(f -> f.name()).collect(Collectors.joining(","));
String positions = fields.stream().map(f -> String.valueOf(f.pos())).collect(Collectors.joining(","));
jobConf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names);
jobConf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, positions);
jobConf.set("partition_columns", "datestr");
// validate record reader compaction // validate record reader compaction
HoodieRealtimeRecordReader recordReader = new HoodieRealtimeRecordReader(split, jobConf, reader); HoodieRealtimeRecordReader recordReader = new HoodieRealtimeRecordReader(split, jobConf, reader);
@@ -502,11 +505,7 @@ public class HoodieRealtimeRecordReaderTest {
assert (firstSchemaFields.containsAll(fields) == false); assert (firstSchemaFields.containsAll(fields) == false);
// Try to read all the fields passed by the new schema // Try to read all the fields passed by the new schema
String names = fields.stream().map(f -> f.name()).collect(Collectors.joining(",")); setHiveColumnNameProps(fields, jobConf, true);
String positions = fields.stream().map(f -> String.valueOf(f.pos())).collect(Collectors.joining(","));
jobConf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names);
jobConf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, positions);
jobConf.set("partition_columns", "datestr");
HoodieRealtimeRecordReader recordReader = null; HoodieRealtimeRecordReader recordReader = null;
try { try {
@@ -518,11 +517,7 @@ public class HoodieRealtimeRecordReaderTest {
} }
// Try to read all the fields passed by the new schema // Try to read all the fields passed by the new schema
names = firstSchemaFields.stream().map(f -> f.name()).collect(Collectors.joining(",")); setHiveColumnNameProps(firstSchemaFields, jobConf, true);
positions = firstSchemaFields.stream().map(f -> String.valueOf(f.pos())).collect(Collectors.joining(","));
jobConf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, names);
jobConf.set(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, positions);
jobConf.set("partition_columns", "datestr");
// This time read only the fields which are part of parquet // This time read only the fields which are part of parquet
recordReader = new HoodieRealtimeRecordReader(split, jobConf, reader); recordReader = new HoodieRealtimeRecordReader(split, jobConf, reader);
// use reader to read base Parquet File and log file // use reader to read base Parquet File and log file