[HUDI-1181] Fix decimal type display issue for record key field (#1953)
* [HUDI-1181] Fix decimal type display issue for record key field * Remove getNestedFieldVal method from DataSourceUtils * resolve comments Co-authored-by: Wenning Ding <wenningd@amazon.com>
This commit is contained in:
@@ -18,10 +18,6 @@
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package org.apache.hudi.avro;
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import org.apache.avro.JsonProperties;
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import java.time.LocalDate;
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import org.apache.avro.LogicalTypes;
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import org.apache.avro.generic.GenericData.Record;
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import org.apache.hudi.common.model.HoodieRecord;
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import org.apache.hudi.common.util.StringUtils;
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import org.apache.hudi.common.util.collection.Pair;
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@@ -29,11 +25,17 @@ import org.apache.hudi.exception.HoodieException;
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import org.apache.hudi.exception.HoodieIOException;
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import org.apache.hudi.exception.SchemaCompatabilityException;
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import org.apache.avro.Conversions.DecimalConversion;
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import org.apache.avro.JsonProperties;
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import org.apache.avro.LogicalTypes;
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import org.apache.avro.LogicalTypes.Decimal;
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import org.apache.avro.Schema;
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import org.apache.avro.Schema.Field;
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import org.apache.avro.generic.GenericData;
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import org.apache.avro.generic.GenericData.Record;
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import org.apache.avro.generic.GenericDatumReader;
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import org.apache.avro.generic.GenericDatumWriter;
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import org.apache.avro.generic.GenericFixed;
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import org.apache.avro.generic.GenericRecord;
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import org.apache.avro.generic.IndexedRecord;
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import org.apache.avro.io.BinaryDecoder;
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@@ -50,7 +52,9 @@ import java.io.ByteArrayOutputStream;
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import java.io.IOException;
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import java.io.InputStream;
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import java.io.OutputStream;
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import java.nio.ByteBuffer;
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import java.nio.charset.StandardCharsets;
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import java.time.LocalDate;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.Collections;
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@@ -433,9 +437,6 @@ public class HoodieAvroUtils {
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/**
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* This method converts values for fields with certain Avro/Parquet data types that require special handling.
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*
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* Logical Date Type is converted to actual Date value instead of Epoch Integer which is how it is
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* represented/stored in parquet.
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*
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* @param fieldSchema avro field schema
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* @param fieldValue avro field value
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* @return field value either converted (for certain data types) or as it is.
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@@ -445,23 +446,44 @@ public class HoodieAvroUtils {
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return fieldValue;
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}
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if (isLogicalTypeDate(fieldSchema)) {
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return LocalDate.ofEpochDay(Long.parseLong(fieldValue.toString()));
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if (fieldSchema.getType() == Schema.Type.UNION) {
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for (Schema schema : fieldSchema.getTypes()) {
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if (schema.getType() != Schema.Type.NULL) {
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return convertValueForAvroLogicalTypes(schema, fieldValue);
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}
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}
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}
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return fieldValue;
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return convertValueForAvroLogicalTypes(fieldSchema, fieldValue);
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}
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/**
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* Given an Avro field schema checks whether the field is of Logical Date Type or not.
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* This method converts values for fields with certain Avro Logical data types that require special handling.
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*
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* Logical Date Type is converted to actual Date value instead of Epoch Integer which is how it is
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* represented/stored in parquet.
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*
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* Decimal Data Type is converted to actual decimal value instead of bytes/fixed which is how it is
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* represented/stored in parquet.
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*
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* @param fieldSchema avro field schema
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* @return boolean indicating whether fieldSchema is of Avro's Date Logical Type
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* @param fieldValue avro field value
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* @return field value either converted (for certain data types) or as it is.
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*/
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private static boolean isLogicalTypeDate(Schema fieldSchema) {
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if (fieldSchema.getType() == Schema.Type.UNION) {
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return fieldSchema.getTypes().stream().anyMatch(schema -> schema.getLogicalType() == LogicalTypes.date());
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private static Object convertValueForAvroLogicalTypes(Schema fieldSchema, Object fieldValue) {
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if (fieldSchema.getLogicalType() == LogicalTypes.date()) {
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return LocalDate.ofEpochDay(Long.parseLong(fieldValue.toString()));
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} else if (fieldSchema.getLogicalType() instanceof LogicalTypes.Decimal) {
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Decimal dc = (Decimal) fieldSchema.getLogicalType();
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DecimalConversion decimalConversion = new DecimalConversion();
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if (fieldSchema.getType() == Schema.Type.FIXED) {
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return decimalConversion.fromFixed((GenericFixed) fieldValue, fieldSchema,
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LogicalTypes.decimal(dc.getPrecision(), dc.getScale()));
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} else if (fieldSchema.getType() == Schema.Type.BYTES) {
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return decimalConversion.fromBytes((ByteBuffer) fieldValue, fieldSchema,
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LogicalTypes.decimal(dc.getPrecision(), dc.getScale()));
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}
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}
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return fieldSchema.getLogicalType() == LogicalTypes.date();
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return fieldValue;
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}
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public static Schema getNullSchema() {
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@@ -43,9 +43,6 @@ import org.apache.hudi.keygen.KeyGenerator;
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import org.apache.hudi.keygen.parser.AbstractHoodieDateTimeParser;
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import org.apache.hudi.table.BulkInsertPartitioner;
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import org.apache.avro.LogicalTypes;
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import org.apache.avro.Schema;
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import org.apache.avro.Schema.Field;
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import org.apache.avro.generic.GenericRecord;
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import org.apache.hadoop.fs.FileSystem;
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import org.apache.hadoop.fs.Path;
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@@ -55,12 +52,10 @@ import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.api.java.JavaSparkContext;
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import java.io.IOException;
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import java.time.LocalDate;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.List;
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import java.util.Map;
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import java.util.stream.Collectors;
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/**
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* Utilities used throughout the data source.
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@@ -69,42 +64,6 @@ public class DataSourceUtils {
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private static final Logger LOG = LogManager.getLogger(DataSourceUtils.class);
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/**
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* Obtain value of the provided field, denoted by dot notation. e.g: a.b.c
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*/
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public static Object getNestedFieldVal(GenericRecord record, String fieldName, boolean returnNullIfNotFound) {
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String[] parts = fieldName.split("\\.");
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GenericRecord valueNode = record;
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int i = 0;
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for (; i < parts.length; i++) {
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String part = parts[i];
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Object val = valueNode.get(part);
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if (val == null) {
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break;
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}
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// return, if last part of name
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if (i == parts.length - 1) {
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Schema fieldSchema = valueNode.getSchema().getField(part).schema();
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return convertValueForSpecificDataTypes(fieldSchema, val);
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} else {
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// VC: Need a test here
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if (!(val instanceof GenericRecord)) {
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throw new HoodieException("Cannot find a record at part value :" + part);
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}
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valueNode = (GenericRecord) val;
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}
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}
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if (returnNullIfNotFound) {
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return null;
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} else {
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throw new HoodieException(
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fieldName + "(Part -" + parts[i] + ") field not found in record. Acceptable fields were :"
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+ valueNode.getSchema().getFields().stream().map(Field::name).collect(Collectors.toList()));
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}
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}
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public static String getTablePath(FileSystem fs, Path[] userProvidedPaths) throws IOException {
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LOG.info("Getting table path..");
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for (Path path : userProvidedPaths) {
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@@ -121,39 +80,6 @@ public class DataSourceUtils {
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throw new TableNotFoundException("Unable to find a hudi table for the user provided paths.");
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}
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/**
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* This method converts values for fields with certain Avro/Parquet data types that require special handling.
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*
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* Logical Date Type is converted to actual Date value instead of Epoch Integer which is how it is represented/stored in parquet.
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*
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* @param fieldSchema avro field schema
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* @param fieldValue avro field value
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* @return field value either converted (for certain data types) or as it is.
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*/
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private static Object convertValueForSpecificDataTypes(Schema fieldSchema, Object fieldValue) {
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if (fieldSchema == null) {
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return fieldValue;
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}
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if (isLogicalTypeDate(fieldSchema)) {
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return LocalDate.ofEpochDay(Long.parseLong(fieldValue.toString()));
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}
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return fieldValue;
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}
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/**
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* Given an Avro field schema checks whether the field is of Logical Date Type or not.
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*
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* @param fieldSchema avro field schema
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* @return boolean indicating whether fieldSchema is of Avro's Date Logical Type
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*/
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private static boolean isLogicalTypeDate(Schema fieldSchema) {
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if (fieldSchema.getType() == Schema.Type.UNION) {
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return fieldSchema.getTypes().stream().anyMatch(schema -> schema.getLogicalType() == LogicalTypes.date());
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}
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return fieldSchema.getLogicalType() == LogicalTypes.date();
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}
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/**
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* Create a key generator class via reflection, passing in any configs needed.
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* <p>
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@@ -28,8 +28,11 @@ import org.apache.hudi.config.HoodieWriteConfig;
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import org.apache.hudi.exception.HoodieException;
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import org.apache.hudi.table.BulkInsertPartitioner;
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import org.apache.avro.Conversions;
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import org.apache.avro.LogicalTypes;
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import org.apache.avro.Schema;
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import org.apache.avro.generic.GenericData;
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import org.apache.avro.generic.GenericFixed;
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import org.apache.avro.generic.GenericRecord;
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import org.apache.spark.api.java.JavaRDD;
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import org.junit.jupiter.api.BeforeEach;
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@@ -40,6 +43,7 @@ import org.mockito.Captor;
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import org.mockito.Mock;
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import org.mockito.junit.jupiter.MockitoExtension;
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import java.math.BigDecimal;
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import java.time.LocalDate;
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import static org.hamcrest.CoreMatchers.containsString;
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@@ -77,13 +81,20 @@ public class TestDataSourceUtils {
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public void testAvroRecordsFieldConversion() {
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// There are fields event_date1, event_date2, event_date3 with logical type as Date. event_date1 & event_date3 are
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// of UNION schema type, which is a union of null and date type in different orders. event_date2 is non-union
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// date type
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// date type. event_cost1, event_cost2, event3 are decimal logical types with UNION schema, which is similar to
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// the event_date.
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String avroSchemaString = "{\"type\": \"record\"," + "\"name\": \"events\"," + "\"fields\": [ "
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+ "{\"name\": \"event_date1\", \"type\" : [{\"type\" : \"int\", \"logicalType\" : \"date\"}, \"null\"]},"
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+ "{\"name\": \"event_date2\", \"type\" : {\"type\": \"int\", \"logicalType\" : \"date\"}},"
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+ "{\"name\": \"event_date3\", \"type\" : [\"null\", {\"type\" : \"int\", \"logicalType\" : \"date\"}]},"
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+ "{\"name\": \"event_name\", \"type\": \"string\"},"
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+ "{\"name\": \"event_organizer\", \"type\": \"string\"}"
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+ "{\"name\": \"event_organizer\", \"type\": \"string\"},"
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+ "{\"name\": \"event_cost1\", \"type\": "
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+ "[{\"type\": \"fixed\", \"name\": \"dc\", \"size\": 5, \"logicalType\": \"decimal\", \"precision\": 10, \"scale\": 6}, \"null\"]},"
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+ "{\"name\": \"event_cost2\", \"type\": "
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+ "{\"type\": \"fixed\", \"name\": \"ef\", \"size\": 5, \"logicalType\": \"decimal\", \"precision\": 10, \"scale\": 6}},"
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+ "{\"name\": \"event_cost3\", \"type\": "
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+ "[\"null\", {\"type\": \"fixed\", \"name\": \"fg\", \"size\": 5, \"logicalType\": \"decimal\", \"precision\": 10, \"scale\": 6}]}"
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+ "]}";
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Schema avroSchema = new Schema.Parser().parse(avroSchemaString);
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@@ -94,6 +105,14 @@ public class TestDataSourceUtils {
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record.put("event_name", "Hudi Meetup");
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record.put("event_organizer", "Hudi PMC");
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BigDecimal bigDecimal = new BigDecimal("123.184331");
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Schema decimalSchema = avroSchema.getField("event_cost1").schema().getTypes().get(0);
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Conversions.DecimalConversion decimalConversions = new Conversions.DecimalConversion();
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GenericFixed genericFixed = decimalConversions.toFixed(bigDecimal, decimalSchema, LogicalTypes.decimal(10, 6));
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record.put("event_cost1", genericFixed);
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record.put("event_cost2", genericFixed);
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record.put("event_cost3", genericFixed);
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assertEquals(LocalDate.ofEpochDay(18000).toString(), HoodieAvroUtils.getNestedFieldValAsString(record, "event_date1",
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true));
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assertEquals(LocalDate.ofEpochDay(18001).toString(), HoodieAvroUtils.getNestedFieldValAsString(record, "event_date2",
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@@ -102,6 +121,9 @@ public class TestDataSourceUtils {
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true));
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assertEquals("Hudi Meetup", HoodieAvroUtils.getNestedFieldValAsString(record, "event_name", true));
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assertEquals("Hudi PMC", HoodieAvroUtils.getNestedFieldValAsString(record, "event_organizer", true));
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assertEquals(bigDecimal.toString(), HoodieAvroUtils.getNestedFieldValAsString(record, "event_cost1", true));
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assertEquals(bigDecimal.toString(), HoodieAvroUtils.getNestedFieldValAsString(record, "event_cost2", true));
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assertEquals(bigDecimal.toString(), HoodieAvroUtils.getNestedFieldValAsString(record, "event_cost3", true));
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}
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@Test
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@@ -20,6 +20,7 @@ package org.apache.hudi.utilities.deltastreamer;
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import org.apache.hudi.AvroConversionUtils;
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import org.apache.hudi.DataSourceUtils;
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import org.apache.hudi.avro.HoodieAvroUtils;
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import org.apache.hudi.client.HoodieWriteClient;
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import org.apache.hudi.client.WriteStatus;
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import org.apache.hudi.common.config.TypedProperties;
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@@ -351,7 +352,7 @@ public class DeltaSync implements Serializable {
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JavaRDD<GenericRecord> avroRDD = avroRDDOptional.get();
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JavaRDD<HoodieRecord> records = avroRDD.map(gr -> {
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HoodieRecordPayload payload = DataSourceUtils.createPayload(cfg.payloadClassName, gr,
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(Comparable) DataSourceUtils.getNestedFieldVal(gr, cfg.sourceOrderingField, false));
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(Comparable) HoodieAvroUtils.getNestedFieldVal(gr, cfg.sourceOrderingField, false));
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return new HoodieRecord<>(keyGenerator.getKey(gr), payload);
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});
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