agrawaldevesh commented on a change in pull request #34659:
URL: https://github.com/apache/spark/pull/34659#discussion_r757856767
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedRleValuesReader.java
##########
@@ -227,30 +230,340 @@ private void readBatchInternal(
switch (mode) {
case RLE:
if (currentValue == state.maxDefinitionLevel) {
- updater.readValues(n, offset, values, valueReader);
- } else {
- nulls.putNulls(offset, n);
+ updater.readValues(n, state.valueOffset, values, valueReader);
+ state.valueOffset += n;
+ } else if (!state.isRequired && currentValue ==
state.maxDefinitionLevel - 1) {
+ // only add null if this represents a null element, but not for
the case where a
+ // struct itself is null
+ nulls.putNulls(state.valueOffset, n);
+ state.valueOffset += n;
}
+ defLevels.putInts(state.levelOffset, n, currentValue);
break;
case PACKED:
for (int i = 0; i < n; ++i) {
- if (currentBuffer[currentBufferIdx++] ==
state.maxDefinitionLevel) {
- updater.readValue(offset + i, values, valueReader);
- } else {
- nulls.putNull(offset + i);
+ int v = currentBuffer[currentBufferIdx++];
+ if (v == state.maxDefinitionLevel) {
+ updater.readValue(state.valueOffset++, values, valueReader);
+ } else if (!state.isRequired && v == state.maxDefinitionLevel -
1) {
+ // only add null if this represents a null element, but not
for the case where a
+ // struct itself is null
+ nulls.putNull(state.valueOffset++);
}
+ defLevels.putInt(state.levelOffset + i, v);
}
break;
}
- offset += n;
+ state.levelOffset += n;
leftInBatch -= n;
rowId += n;
leftInPage -= n;
currentCount -= n;
+ defLevels.addElementsAppended(n);
+ }
+ }
+
+ state.rowsToReadInBatch = leftInBatch;
+ state.valuesToReadInPage = leftInPage;
+ state.rowId = rowId;
+ }
+
+ public void readBatchNested(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ VectorizedValuesReader valueReader,
+ ParquetVectorUpdater updater) {
+ readBatchNestedInternal(state, repLevels, defLevelsReader, defLevels,
values, values, true,
+ valueReader, updater);
+ }
+
+ public void readIntegersNested(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ WritableColumnVector nulls,
+ VectorizedValuesReader valueReader) {
+ readBatchNestedInternal(state, repLevels, defLevelsReader, defLevels,
values, nulls, false,
+ valueReader, new ParquetVectorUpdaterFactory.IntegerUpdater());
+ }
+
+ /**
+ * Keep reading repetition level values from the page until either: 1) we've
read enough
+ * top-level rows to fill the current batch, or 2) we've drained the data
page completely.
+ *
+ * @param valuesReused whether 'values' vector is reused for 'nulls'
+ */
+ public void readBatchNestedInternal(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ WritableColumnVector nulls,
+ boolean valuesReused,
+ VectorizedValuesReader valueReader,
+ ParquetVectorUpdater updater) {
+
+ int leftInBatch = state.rowsToReadInBatch;
+ int leftInPage = state.valuesToReadInPage;
+ long rowId = state.rowId;
+
+ DefLevelProcessor defLevelProcessor = new
DefLevelProcessor(defLevelsReader, state, defLevels,
+ values, nulls, valuesReused, valueReader, updater);
+
+ while ((leftInBatch > 0 || !state.lastListCompleted) && leftInPage > 0) {
+ if (currentCount == 0 && !readNextGroup()) break;
+
+ // values to read in the current RLE/PACKED block, must be <= what's
left in the page
+ int valuesLeftInBlock = Math.min(leftInPage, currentCount);
+
+ // the current row range start and end
+ long rangeStart = state.currentRangeStart();
+ long rangeEnd = state.currentRangeEnd();
+
+ switch (mode) {
+ case RLE:
+ // this RLE block is consist of top-level rows, so we'll need to
check
+ // if the rows should be skipped according to row indexes.
+ if (currentValue == 0) {
+ if (leftInBatch == 0) {
+ state.lastListCompleted = true;
+ } else {
+ // # of rows to read in the block, must be <= what's left in the
current batch
+ int n = Math.min(leftInBatch, valuesLeftInBlock);
+
+ if (rowId + n < rangeStart) {
+ // need to skip all rows in [rowId, rowId + n)
+ defLevelProcessor.skipValues(n);
+ rowId += n;
+ currentCount -= n;
+ leftInPage -= n;
Review comment:
should leftInBatch be updated here as well ?
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedRleValuesReader.java
##########
@@ -227,30 +230,340 @@ private void readBatchInternal(
switch (mode) {
case RLE:
if (currentValue == state.maxDefinitionLevel) {
- updater.readValues(n, offset, values, valueReader);
- } else {
- nulls.putNulls(offset, n);
+ updater.readValues(n, state.valueOffset, values, valueReader);
+ state.valueOffset += n;
+ } else if (!state.isRequired && currentValue ==
state.maxDefinitionLevel - 1) {
+ // only add null if this represents a null element, but not for
the case where a
+ // struct itself is null
+ nulls.putNulls(state.valueOffset, n);
+ state.valueOffset += n;
}
+ defLevels.putInts(state.levelOffset, n, currentValue);
break;
case PACKED:
for (int i = 0; i < n; ++i) {
- if (currentBuffer[currentBufferIdx++] ==
state.maxDefinitionLevel) {
- updater.readValue(offset + i, values, valueReader);
- } else {
- nulls.putNull(offset + i);
+ int v = currentBuffer[currentBufferIdx++];
+ if (v == state.maxDefinitionLevel) {
+ updater.readValue(state.valueOffset++, values, valueReader);
+ } else if (!state.isRequired && v == state.maxDefinitionLevel -
1) {
+ // only add null if this represents a null element, but not
for the case where a
+ // struct itself is null
+ nulls.putNull(state.valueOffset++);
}
+ defLevels.putInt(state.levelOffset + i, v);
}
break;
}
- offset += n;
+ state.levelOffset += n;
leftInBatch -= n;
rowId += n;
leftInPage -= n;
currentCount -= n;
+ defLevels.addElementsAppended(n);
+ }
+ }
+
+ state.rowsToReadInBatch = leftInBatch;
+ state.valuesToReadInPage = leftInPage;
+ state.rowId = rowId;
+ }
+
+ public void readBatchNested(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ VectorizedValuesReader valueReader,
+ ParquetVectorUpdater updater) {
+ readBatchNestedInternal(state, repLevels, defLevelsReader, defLevels,
values, values, true,
+ valueReader, updater);
+ }
+
+ public void readIntegersNested(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ WritableColumnVector nulls,
+ VectorizedValuesReader valueReader) {
+ readBatchNestedInternal(state, repLevels, defLevelsReader, defLevels,
values, nulls, false,
+ valueReader, new ParquetVectorUpdaterFactory.IntegerUpdater());
+ }
+
+ /**
+ * Keep reading repetition level values from the page until either: 1) we've
read enough
+ * top-level rows to fill the current batch, or 2) we've drained the data
page completely.
+ *
+ * @param valuesReused whether 'values' vector is reused for 'nulls'
+ */
+ public void readBatchNestedInternal(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ WritableColumnVector nulls,
+ boolean valuesReused,
+ VectorizedValuesReader valueReader,
+ ParquetVectorUpdater updater) {
+
+ int leftInBatch = state.rowsToReadInBatch;
+ int leftInPage = state.valuesToReadInPage;
+ long rowId = state.rowId;
+
+ DefLevelProcessor defLevelProcessor = new
DefLevelProcessor(defLevelsReader, state, defLevels,
+ values, nulls, valuesReused, valueReader, updater);
+
+ while ((leftInBatch > 0 || !state.lastListCompleted) && leftInPage > 0) {
+ if (currentCount == 0 && !readNextGroup()) break;
+
+ // values to read in the current RLE/PACKED block, must be <= what's
left in the page
+ int valuesLeftInBlock = Math.min(leftInPage, currentCount);
+
+ // the current row range start and end
+ long rangeStart = state.currentRangeStart();
+ long rangeEnd = state.currentRangeEnd();
+
+ switch (mode) {
+ case RLE:
+ // this RLE block is consist of top-level rows, so we'll need to
check
+ // if the rows should be skipped according to row indexes.
+ if (currentValue == 0) {
+ if (leftInBatch == 0) {
+ state.lastListCompleted = true;
+ } else {
+ // # of rows to read in the block, must be <= what's left in the
current batch
+ int n = Math.min(leftInBatch, valuesLeftInBlock);
+
+ if (rowId + n < rangeStart) {
+ // need to skip all rows in [rowId, rowId + n)
+ defLevelProcessor.skipValues(n);
+ rowId += n;
+ currentCount -= n;
+ leftInPage -= n;
+ } else if (rowId > rangeEnd) {
+ // the current row index already beyond the current range:
move to the next range
+ // and repeat
+ state.nextRange();
+ } else {
+ // the range [rowId, rowId + n) overlaps with the current row
range
+ long start = Math.max(rangeStart, rowId);
+ long end = Math.min(rangeEnd, rowId + n - 1);
+
+ // skip the rows in [rowId, start)
+ int toSkip = (int) (start - rowId);
+ if (toSkip > 0) {
+ defLevelProcessor.skipValues(toSkip);
+ rowId += toSkip;
+ currentCount -= toSkip;
+ leftInPage -= toSkip;
+ }
+
+ // read the rows in [start, end]
+ n = (int) (end - start + 1);
+
+ leftInBatch -= n;
+ if (n > 0) {
+ repLevels.appendInts(n, 0);
+ defLevelProcessor.readValues(n);
+ }
+
+ rowId += n;
Review comment:
lets move leftInBatch update here.
I am not sure why some of these are local variables vs member vars :-)
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedRleValuesReader.java
##########
@@ -227,30 +230,340 @@ private void readBatchInternal(
switch (mode) {
case RLE:
if (currentValue == state.maxDefinitionLevel) {
- updater.readValues(n, offset, values, valueReader);
- } else {
- nulls.putNulls(offset, n);
+ updater.readValues(n, state.valueOffset, values, valueReader);
+ state.valueOffset += n;
+ } else if (!state.isRequired && currentValue ==
state.maxDefinitionLevel - 1) {
+ // only add null if this represents a null element, but not for
the case where a
+ // struct itself is null
+ nulls.putNulls(state.valueOffset, n);
+ state.valueOffset += n;
}
+ defLevels.putInts(state.levelOffset, n, currentValue);
break;
case PACKED:
for (int i = 0; i < n; ++i) {
- if (currentBuffer[currentBufferIdx++] ==
state.maxDefinitionLevel) {
- updater.readValue(offset + i, values, valueReader);
- } else {
- nulls.putNull(offset + i);
+ int v = currentBuffer[currentBufferIdx++];
+ if (v == state.maxDefinitionLevel) {
+ updater.readValue(state.valueOffset++, values, valueReader);
+ } else if (!state.isRequired && v == state.maxDefinitionLevel -
1) {
+ // only add null if this represents a null element, but not
for the case where a
+ // struct itself is null
+ nulls.putNull(state.valueOffset++);
}
+ defLevels.putInt(state.levelOffset + i, v);
}
break;
}
- offset += n;
+ state.levelOffset += n;
leftInBatch -= n;
rowId += n;
leftInPage -= n;
currentCount -= n;
+ defLevels.addElementsAppended(n);
+ }
+ }
+
+ state.rowsToReadInBatch = leftInBatch;
+ state.valuesToReadInPage = leftInPage;
+ state.rowId = rowId;
+ }
+
+ public void readBatchNested(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ VectorizedValuesReader valueReader,
+ ParquetVectorUpdater updater) {
+ readBatchNestedInternal(state, repLevels, defLevelsReader, defLevels,
values, values, true,
+ valueReader, updater);
+ }
+
+ public void readIntegersNested(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ WritableColumnVector nulls,
+ VectorizedValuesReader valueReader) {
+ readBatchNestedInternal(state, repLevels, defLevelsReader, defLevels,
values, nulls, false,
+ valueReader, new ParquetVectorUpdaterFactory.IntegerUpdater());
+ }
+
+ /**
+ * Keep reading repetition level values from the page until either: 1) we've
read enough
+ * top-level rows to fill the current batch, or 2) we've drained the data
page completely.
+ *
+ * @param valuesReused whether 'values' vector is reused for 'nulls'
+ */
+ public void readBatchNestedInternal(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ WritableColumnVector nulls,
+ boolean valuesReused,
+ VectorizedValuesReader valueReader,
+ ParquetVectorUpdater updater) {
+
+ int leftInBatch = state.rowsToReadInBatch;
+ int leftInPage = state.valuesToReadInPage;
+ long rowId = state.rowId;
+
+ DefLevelProcessor defLevelProcessor = new
DefLevelProcessor(defLevelsReader, state, defLevels,
+ values, nulls, valuesReused, valueReader, updater);
+
+ while ((leftInBatch > 0 || !state.lastListCompleted) && leftInPage > 0) {
+ if (currentCount == 0 && !readNextGroup()) break;
Review comment:
The original non nested code refers to currentCount as this.currentCount
in certain places. Should we pick that style as well ?
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
##########
@@ -897,6 +897,14 @@ object SQLConf {
.booleanConf
.createWithDefault(true)
+ val PARQUET_VECTORIZED_READER_NESTED_COLUMN_ENABLED =
+ buildConf("spark.sql.parquet.enableNestedColumnVectorizedReader")
+ .doc("Enables vectorized Parquet decoding for nested columns (e.g.,
struct, list, map). " +
+ s"Note to enable this ${PARQUET_VECTORIZED_READER_ENABLED.key} also
needs to be enabled.")
+ .version("3.3.0")
+ .booleanConf
+ .createWithDefault(true)
Review comment:
I am liking the confidence :-)
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedRleValuesReader.java
##########
@@ -227,30 +230,340 @@ private void readBatchInternal(
switch (mode) {
case RLE:
if (currentValue == state.maxDefinitionLevel) {
- updater.readValues(n, offset, values, valueReader);
- } else {
- nulls.putNulls(offset, n);
+ updater.readValues(n, state.valueOffset, values, valueReader);
+ state.valueOffset += n;
+ } else if (!state.isRequired && currentValue ==
state.maxDefinitionLevel - 1) {
+ // only add null if this represents a null element, but not for
the case where a
+ // struct itself is null
+ nulls.putNulls(state.valueOffset, n);
+ state.valueOffset += n;
}
+ defLevels.putInts(state.levelOffset, n, currentValue);
break;
case PACKED:
for (int i = 0; i < n; ++i) {
- if (currentBuffer[currentBufferIdx++] ==
state.maxDefinitionLevel) {
- updater.readValue(offset + i, values, valueReader);
- } else {
- nulls.putNull(offset + i);
+ int v = currentBuffer[currentBufferIdx++];
Review comment:
v -> value ?
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedRleValuesReader.java
##########
@@ -227,30 +230,340 @@ private void readBatchInternal(
switch (mode) {
case RLE:
if (currentValue == state.maxDefinitionLevel) {
- updater.readValues(n, offset, values, valueReader);
- } else {
- nulls.putNulls(offset, n);
+ updater.readValues(n, state.valueOffset, values, valueReader);
+ state.valueOffset += n;
+ } else if (!state.isRequired && currentValue ==
state.maxDefinitionLevel - 1) {
+ // only add null if this represents a null element, but not for
the case where a
+ // struct itself is null
+ nulls.putNulls(state.valueOffset, n);
+ state.valueOffset += n;
}
+ defLevels.putInts(state.levelOffset, n, currentValue);
break;
case PACKED:
for (int i = 0; i < n; ++i) {
- if (currentBuffer[currentBufferIdx++] ==
state.maxDefinitionLevel) {
- updater.readValue(offset + i, values, valueReader);
- } else {
- nulls.putNull(offset + i);
+ int v = currentBuffer[currentBufferIdx++];
+ if (v == state.maxDefinitionLevel) {
+ updater.readValue(state.valueOffset++, values, valueReader);
+ } else if (!state.isRequired && v == state.maxDefinitionLevel -
1) {
+ // only add null if this represents a null element, but not
for the case where a
+ // struct itself is null
+ nulls.putNull(state.valueOffset++);
}
+ defLevels.putInt(state.levelOffset + i, v);
}
break;
}
- offset += n;
+ state.levelOffset += n;
leftInBatch -= n;
rowId += n;
leftInPage -= n;
currentCount -= n;
+ defLevels.addElementsAppended(n);
+ }
+ }
+
+ state.rowsToReadInBatch = leftInBatch;
+ state.valuesToReadInPage = leftInPage;
+ state.rowId = rowId;
+ }
+
+ public void readBatchNested(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ VectorizedValuesReader valueReader,
+ ParquetVectorUpdater updater) {
+ readBatchNestedInternal(state, repLevels, defLevelsReader, defLevels,
values, values, true,
+ valueReader, updater);
+ }
+
+ public void readIntegersNested(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ WritableColumnVector nulls,
+ VectorizedValuesReader valueReader) {
+ readBatchNestedInternal(state, repLevels, defLevelsReader, defLevels,
values, nulls, false,
+ valueReader, new ParquetVectorUpdaterFactory.IntegerUpdater());
+ }
+
+ /**
+ * Keep reading repetition level values from the page until either: 1) we've
read enough
+ * top-level rows to fill the current batch, or 2) we've drained the data
page completely.
+ *
+ * @param valuesReused whether 'values' vector is reused for 'nulls'
+ */
+ public void readBatchNestedInternal(
+ ParquetReadState state,
+ WritableColumnVector repLevels,
+ VectorizedRleValuesReader defLevelsReader,
+ WritableColumnVector defLevels,
+ WritableColumnVector values,
+ WritableColumnVector nulls,
+ boolean valuesReused,
+ VectorizedValuesReader valueReader,
+ ParquetVectorUpdater updater) {
+
+ int leftInBatch = state.rowsToReadInBatch;
+ int leftInPage = state.valuesToReadInPage;
+ long rowId = state.rowId;
+
+ DefLevelProcessor defLevelProcessor = new
DefLevelProcessor(defLevelsReader, state, defLevels,
+ values, nulls, valuesReused, valueReader, updater);
+
+ while ((leftInBatch > 0 || !state.lastListCompleted) && leftInPage > 0) {
+ if (currentCount == 0 && !readNextGroup()) break;
+
+ // values to read in the current RLE/PACKED block, must be <= what's
left in the page
+ int valuesLeftInBlock = Math.min(leftInPage, currentCount);
+
+ // the current row range start and end
+ long rangeStart = state.currentRangeStart();
+ long rangeEnd = state.currentRangeEnd();
+
+ switch (mode) {
+ case RLE:
+ // this RLE block is consist of top-level rows, so we'll need to
check
+ // if the rows should be skipped according to row indexes.
+ if (currentValue == 0) {
+ if (leftInBatch == 0) {
+ state.lastListCompleted = true;
+ } else {
+ // # of rows to read in the block, must be <= what's left in the
current batch
+ int n = Math.min(leftInBatch, valuesLeftInBlock);
+
+ if (rowId + n < rangeStart) {
+ // need to skip all rows in [rowId, rowId + n)
+ defLevelProcessor.skipValues(n);
+ rowId += n;
+ currentCount -= n;
+ leftInPage -= n;
+ } else if (rowId > rangeEnd) {
+ // the current row index already beyond the current range:
move to the next range
+ // and repeat
+ state.nextRange();
+ } else {
+ // the range [rowId, rowId + n) overlaps with the current row
range
+ long start = Math.max(rangeStart, rowId);
+ long end = Math.min(rangeEnd, rowId + n - 1);
+
+ // skip the rows in [rowId, start)
+ int toSkip = (int) (start - rowId);
+ if (toSkip > 0) {
+ defLevelProcessor.skipValues(toSkip);
+ rowId += toSkip;
+ currentCount -= toSkip;
+ leftInPage -= toSkip;
+ }
+
+ // read the rows in [start, end]
+ n = (int) (end - start + 1);
+
+ leftInBatch -= n;
+ if (n > 0) {
+ repLevels.appendInts(n, 0);
Review comment:
Not sure, but should repLevels be immutable for the purpose of this
function ?
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedRleValuesReader.java
##########
@@ -227,30 +230,340 @@ private void readBatchInternal(
switch (mode) {
case RLE:
Review comment:
I am not too familiar with this code, but where do we assert that these
are the only two encoding choices. There is no default arm in this case.
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