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Prune Non-referenced Fields from Nested RowTypes
This set of changes prunes nested RowTypes to only the fields that are actually dereferenced in the users' projections. The Parquet implementation already solves for this, but it works on it's own abstractions so it's not fit for use in the other Hive formats. I believe this approach could be adopted by the Parquet PageSource as well, thereby simplifying, but I don't want to bite that off now. I believe the approach will work for Avro as well, but the PageSource isn't plumbing the inferred reader schema down to the type resolver: it is just passing the selected columns from the writer schema as both reader and writer. I added a test that proves it works well for OpenXJson because it is simple to mock data for it and it supports position-based deserialization: a JSON Array into a Row.
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90 changes: 90 additions & 0 deletions
90
lib/trino-hive-formats/src/main/java/io/trino/hive/formats/SparseRowType.java
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| @@ -0,0 +1,90 @@ | ||
| /* | ||
| * Licensed 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 io.trino.hive.formats; | ||
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| import com.google.common.collect.ImmutableList; | ||
| import io.trino.spi.type.RowType; | ||
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| import java.util.List; | ||
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| import static com.google.common.base.Preconditions.checkArgument; | ||
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| /** | ||
| * A SparseRowType is a RowType that conveys which fields are active in a row. | ||
| * <p> | ||
| * It manages the positional mapping between the "sparse" fields and the | ||
| * underlying "dense" RowType. It allows position-based deserializers to know | ||
| * the complete schema for a Row while also knowing which fields need to be | ||
| * deserialized. Name-based deserializers can simply use the dense fields | ||
| * from the underlying RowType. | ||
| */ | ||
| public class SparseRowType | ||
| extends RowType | ||
| { | ||
| private final List<Field> sparseFields; | ||
| private final int[] offsets; | ||
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| private SparseRowType(List<Field> sparseFields, List<Field> denseFields, int[] offsets) | ||
| { | ||
| super(makeSignature(denseFields), denseFields); | ||
| this.sparseFields = sparseFields; | ||
| this.offsets = offsets; | ||
| } | ||
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| /** | ||
| * Create a SparseRowType from a list of fields and a mask indicating which fields are active. | ||
| */ | ||
| public static SparseRowType from(List<Field> fields, boolean[] mask) | ||
| { | ||
| checkArgument(fields.size() == mask.length); | ||
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| int[] offsets = new int[fields.size()]; | ||
| ImmutableList.Builder<Field> denseFields = ImmutableList.builder(); | ||
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| int offset = 0; | ||
| for (int i = 0; i < mask.length; i++) { | ||
| if (mask[i]) { | ||
| denseFields.add(fields.get(i)); | ||
| offsets[i] = offset++; | ||
| } | ||
| else { | ||
| offsets[i] = -1; | ||
| } | ||
| } | ||
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| return new SparseRowType(ImmutableList.copyOf(fields), denseFields.build(), offsets); | ||
| } | ||
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| public static SparseRowType initial(List<Field> fields, Integer activeField) | ||
| { | ||
| boolean[] mask = new boolean[fields.size()]; | ||
| mask[activeField] = true; | ||
| return SparseRowType.from(fields, mask); | ||
| } | ||
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| public List<Field> getSparseFields() | ||
| { | ||
| return sparseFields; | ||
| } | ||
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| /** | ||
| * Get the offset to the dense field for the sparseField at sparsePosition. | ||
| */ | ||
| public Integer getOffset(int sparsePosition) | ||
| { | ||
| return offsets[sparsePosition] >= 0 | ||
| ? offsets[sparsePosition] | ||
| : null; | ||
| } | ||
| } |
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