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[SPARK-5938] [SPARK-5443] [SQL] Improve JsonRDD performance
This patch comprises of a few related pieces of work:
* Schema inference is performed directly on the JSON token stream
* `String => Row` conversion populate Spark SQL structures without intermediate types
* Projection pushdown is implemented via CatalystScan for DataFrame queries
* Support for the legacy parser by setting `spark.sql.json.useJacksonStreamingAPI` to `false`
Performance improvements depend on the schema and queries being executed, but it should be faster across the board. Below are benchmarks using the last.fm Million Song dataset:
```
Command | Baseline | Patched
---------------------------------------------------|----------|--------
import sqlContext.implicits._ | |
val df = sqlContext.jsonFile("/tmp/lastfm.json") | 70.0s | 14.6s
df.count() | 28.8s | 6.2s
df.rdd.count() | 35.3s | 21.5s
df.where($"artist" === "Robert Hood").collect() | 28.3s | 16.9s
```
To prepare this dataset for benchmarking, follow these steps:
```
# Fetch the datasets from http://labrosa.ee.columbia.edu/millionsong/lastfm
wget http://labrosa.ee.columbia.edu/millionsong/sites/default/files/lastfm/lastfm_test.zip \
http://labrosa.ee.columbia.edu/millionsong/sites/default/files/lastfm/lastfm_train.zip
# Decompress and combine, pipe through `jq -c` to ensure there is one record per line
unzip -p lastfm_test.zip lastfm_train.zip | jq -c . > lastfm.json
```
Author: Nathan Howell <nhowell@godaddy.com>
Closes apache#5801 from NathanHowell/json-performance and squashes the following commits:
26fea31 [Nathan Howell] Recreate the baseRDD each for each scan operation
a7ebeb2 [Nathan Howell] Increase coverage of inserts into a JSONRelation
e06a1dd [Nathan Howell] Add comments to the `useJacksonStreamingAPI` config flag
6822712 [Nathan Howell] Split up JsonRDD2 into multiple objects
fa8234f [Nathan Howell] Wrap long lines
b31917b [Nathan Howell] Rename `useJsonRDD2` to `useJacksonStreamingAPI`
15c5d1b [Nathan Howell] JSONRelation's baseRDD need not be lazy
f8add6e [Nathan Howell] Add comments on lack of support for precision and scale DecimalTypes
fa0be47 [Nathan Howell] Remove unused default case in the field parser
80dba17 [Nathan Howell] Add comments regarding null handling and empty strings
842846d [Nathan Howell] Point the empty schema inference test at JsonRDD2
ab6ee87 [Nathan Howell] Add projection pushdown support to JsonRDD/JsonRDD2
f636c14 [Nathan Howell] Enable JsonRDD2 by default, add a flag to switch back to JsonRDD
0bbc445 [Nathan Howell] Improve JSON parsing and type inference performance
7ca70c1 [Nathan Howell] Eliminate arrow pattern, replace with pattern matches- Loading branch information
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171
sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
|
|
||
| package org.apache.spark.sql.json | ||
|
|
||
| import com.fasterxml.jackson.core._ | ||
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|
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| import org.apache.spark.rdd.RDD | ||
| import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion | ||
| import org.apache.spark.sql.json.JacksonUtils.nextUntil | ||
| import org.apache.spark.sql.types._ | ||
|
|
||
| private[sql] object InferSchema { | ||
| /** | ||
| * Infer the type of a collection of json records in three stages: | ||
| * 1. Infer the type of each record | ||
| * 2. Merge types by choosing the lowest type necessary to cover equal keys | ||
| * 3. Replace any remaining null fields with string, the top type | ||
| */ | ||
| def apply( | ||
| json: RDD[String], | ||
| samplingRatio: Double = 1.0, | ||
| columnNameOfCorruptRecords: String): StructType = { | ||
| require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0") | ||
| val schemaData = if (samplingRatio > 0.99) { | ||
| json | ||
| } else { | ||
| json.sample(withReplacement = false, samplingRatio, 1) | ||
| } | ||
|
|
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| // perform schema inference on each row and merge afterwards | ||
| schemaData.mapPartitions { iter => | ||
| val factory = new JsonFactory() | ||
| iter.map { row => | ||
| try { | ||
| val parser = factory.createParser(row) | ||
| parser.nextToken() | ||
| inferField(parser) | ||
| } catch { | ||
| case _: JsonParseException => | ||
| StructType(Seq(StructField(columnNameOfCorruptRecords, StringType))) | ||
| } | ||
| } | ||
| }.treeAggregate[DataType](StructType(Seq()))(compatibleRootType, compatibleRootType) match { | ||
| case st: StructType => nullTypeToStringType(st) | ||
| } | ||
| } | ||
|
|
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| /** | ||
| * Infer the type of a json document from the parser's token stream | ||
| */ | ||
| private def inferField(parser: JsonParser): DataType = { | ||
| import com.fasterxml.jackson.core.JsonToken._ | ||
| parser.getCurrentToken match { | ||
| case null | VALUE_NULL => NullType | ||
|
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| case FIELD_NAME => | ||
| parser.nextToken() | ||
| inferField(parser) | ||
|
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| case VALUE_STRING if parser.getTextLength < 1 => | ||
| // Zero length strings and nulls have special handling to deal | ||
| // with JSON generators that do not distinguish between the two. | ||
| // To accurately infer types for empty strings that are really | ||
| // meant to represent nulls we assume that the two are isomorphic | ||
| // but will defer treating null fields as strings until all the | ||
| // record fields' types have been combined. | ||
| NullType | ||
|
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| case VALUE_STRING => StringType | ||
| case START_OBJECT => | ||
| val builder = Seq.newBuilder[StructField] | ||
| while (nextUntil(parser, END_OBJECT)) { | ||
| builder += StructField(parser.getCurrentName, inferField(parser), nullable = true) | ||
| } | ||
|
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| StructType(builder.result().sortBy(_.name)) | ||
|
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| case START_ARRAY => | ||
| // If this JSON array is empty, we use NullType as a placeholder. | ||
| // If this array is not empty in other JSON objects, we can resolve | ||
| // the type as we pass through all JSON objects. | ||
| var elementType: DataType = NullType | ||
| while (nextUntil(parser, END_ARRAY)) { | ||
| elementType = compatibleType(elementType, inferField(parser)) | ||
| } | ||
|
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| ArrayType(elementType) | ||
|
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| case VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT => | ||
| import JsonParser.NumberType._ | ||
| parser.getNumberType match { | ||
| // For Integer values, use LongType by default. | ||
| case INT | LONG => LongType | ||
| // Since we do not have a data type backed by BigInteger, | ||
| // when we see a Java BigInteger, we use DecimalType. | ||
| case BIG_INTEGER | BIG_DECIMAL => DecimalType.Unlimited | ||
| case FLOAT | DOUBLE => DoubleType | ||
| } | ||
|
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| case VALUE_TRUE | VALUE_FALSE => BooleanType | ||
| } | ||
| } | ||
|
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| private def nullTypeToStringType(struct: StructType): StructType = { | ||
| val fields = struct.fields.map { | ||
| case StructField(fieldName, dataType, nullable, _) => | ||
| val newType = dataType match { | ||
| case NullType => StringType | ||
| case ArrayType(NullType, containsNull) => ArrayType(StringType, containsNull) | ||
| case ArrayType(struct: StructType, containsNull) => | ||
| ArrayType(nullTypeToStringType(struct), containsNull) | ||
| case struct: StructType =>nullTypeToStringType(struct) | ||
| case other: DataType => other | ||
| } | ||
|
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| StructField(fieldName, newType, nullable) | ||
| } | ||
|
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| StructType(fields) | ||
| } | ||
|
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| /** | ||
| * Remove top-level ArrayType wrappers and merge the remaining schemas | ||
| */ | ||
| private def compatibleRootType: (DataType, DataType) => DataType = { | ||
| case (ArrayType(ty1, _), ty2) => compatibleRootType(ty1, ty2) | ||
| case (ty1, ArrayType(ty2, _)) => compatibleRootType(ty1, ty2) | ||
| case (ty1, ty2) => compatibleType(ty1, ty2) | ||
| } | ||
|
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| /** | ||
| * Returns the most general data type for two given data types. | ||
| */ | ||
| private[json] def compatibleType(t1: DataType, t2: DataType): DataType = { | ||
| HiveTypeCoercion.findTightestCommonType(t1, t2).getOrElse { | ||
| // t1 or t2 is a StructType, ArrayType, or an unexpected type. | ||
| (t1, t2) match { | ||
| case (other: DataType, NullType) => other | ||
| case (NullType, other: DataType) => other | ||
| case (StructType(fields1), StructType(fields2)) => | ||
| val newFields = (fields1 ++ fields2).groupBy(field => field.name).map { | ||
| case (name, fieldTypes) => | ||
| val dataType = fieldTypes.view.map(_.dataType).reduce(compatibleType) | ||
| StructField(name, dataType, nullable = true) | ||
| } | ||
| StructType(newFields.toSeq.sortBy(_.name)) | ||
|
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| case (ArrayType(elementType1, containsNull1), ArrayType(elementType2, containsNull2)) => | ||
| ArrayType(compatibleType(elementType1, elementType2), containsNull1 || containsNull2) | ||
|
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| // strings and every string is a Json object. | ||
| case (_, _) => StringType | ||
| } | ||
| } | ||
| } | ||
| } |
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