andygrove commented on code in PR #5051: URL: https://github.com/apache/datafusion-comet/pull/5051#discussion_r3867744580
########## spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowCachedBatchSerializer.scala: ########## @@ -0,0 +1,512 @@ +/* + * 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.comet.execution.arrow + +import scala.collection.JavaConverters._ + +import org.apache.spark.TaskContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, GenericInternalRow, IsNotNull, IsNull, UnsafeProjection} +import org.apache.spark.sql.columnar.{CachedBatch, SimpleMetricsCachedBatch, SimpleMetricsCachedBatchSerializer} +import org.apache.spark.sql.comet.util.Utils +import org.apache.spark.sql.execution.columnar.DefaultCachedBatchSerializer +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnVector} +import org.apache.spark.storage.StorageLevel +import org.apache.spark.unsafe.types.{ByteArray, UTF8String} +import org.apache.spark.util.io.ChunkedByteBuffer + +import org.apache.comet.CometArrowAllocator + +/** + * Cached batch format used when Comet writes Spark in-memory cache data. + * + * `columns` holds one compressed Arrow stream per cached column, in cache-schema order, produced + * by `Utils.serializeBatchColumns`. Storing columns separately is what lets a scan decode only + * the ones it projected; a single stream covering the whole batch would have to be inflated in + * full before any projection could be applied. The cache manager still owns storage and eviction; + * this class only changes the cached payload. + */ +private case class CometCachedBatch( + override val numRows: Int, + override val sizeInBytes: Long, + override val stats: InternalRow, + columns: Array[ChunkedByteBuffer]) + extends SimpleMetricsCachedBatch + +/** + * Cache serializer that stores Comet-compatible Arrow batches in Spark's in-memory cache. + * + * The cached payload format is decided by the schema alone. A relation whose schema Comet's Arrow + * writer supports is stored as `CometCachedBatch`, and every other relation is delegated in full + * to Spark's `DefaultCachedBatchSerializer`. The format deliberately does not depend on any + * runtime config: `spark.sql.cache.serializer` is a static conf, so installing this serializer is + * already a per-application decision, and a relation whose format could flip mid-session cannot + * be read back reliably. `spark.comet.exec.inMemoryCache.enabled` still governs whether a scan + * over the cache runs natively, and its value at startup is what makes `CometDriverPlugin` + * install this serializer in the first place. + * + * Reads of `CometCachedBatch` keep working when the native scan is disabled, because Spark then + * reads the same cached data through the SparkToColumnar fallback path. + */ +class ArrowCachedBatchSerializer extends SimpleMetricsCachedBatchSerializer { + + import ArrowCachedBatchSerializer.supportsSchema + + private val fallback = new DefaultCachedBatchSerializer() + + // Bounds and null counts per column, gathered before the batch is serialized: serializing + // clears the batch's vectors, and the per-column byte sizes that complete the statistics row + // are only known afterwards. See statsRow. + private def gatherColumnStats( + batch: ColumnarBatch, + attrs: Seq[Attribute]): (Array[Any], Array[Any], Array[Int]) = { + val numCols = attrs.length + val lower = new Array[Any](numCols) + val upper = new Array[Any](numCols) + val nulls = Array.fill[Int](numCols)(0) + val numRows = batch.numRows() + + var c = 0 + while (c < numCols) { + val dt = attrs(c).dataType + val col = batch.column(c) + var r = 0 + while (r < numRows) { + if (col.isNullAt(r)) { + nulls(c) += 1 + } else if (tracksBounds(dt)) { + val value = readValue(col, dt, r) + if (lower(c) == null || compare(dt, value, lower(c)) < 0) { + lower(c) = value + } + if (upper(c) == null || compare(dt, value, upper(c)) > 0) { + upper(c) = value + } + } + r += 1 + } + c += 1 + } + + (lower, upper, nulls) + } + + // Build the statistics row expected by SimpleMetricsCachedBatchSerializer. + // For each cached column Spark expects five values in this order: + // lower bound, upper bound, null count, row count, and size in bytes. + private def statsRow( + lower: Array[Any], + upper: Array[Any], + nulls: Array[Int], + numRows: Int, + columnSizes: Array[Long]): InternalRow = { + val numCols = lower.length + val values = new Array[Any](numCols * 5) + var c = 0 + while (c < numCols) { + val base = c * 5 + values(base) = lower(c) + values(base + 1) = upper(c) + values(base + 2) = nulls(c) + values(base + 3) = numRows + // Each column is its own compressed stream, so its size is known exactly. Cache pruning + // uses bounds/null-count/row-count rather than this field, but Spark reserves it and + // reports it, so record the real value. + values(base + 4) = columnSizes(c) + c += 1 + } + + new GenericInternalRow(values) + } + + // Spark can prune cache batches only for types whose bounds can be compared. + // Other types still report null count and row count but leave bounds as null. + private def tracksBounds(dt: DataType): Boolean = dt match { + case BooleanType | ByteType | ShortType | IntegerType | LongType | FloatType | DoubleType | + _: DecimalType | StringType | DateType | TimestampType | TimestampNTZType => + true + case _ => false + } + + // Read a non-null value from a ColumnVector using Spark's internal value type + // for the corresponding DataType. + private def readValue(col: ColumnVector, dt: DataType, rowId: Int): Any = dt match { + case BooleanType => col.getBoolean(rowId) + case ByteType => col.getByte(rowId) + case ShortType => col.getShort(rowId) + case IntegerType | DateType => col.getInt(rowId) + case LongType | TimestampType | TimestampNTZType => col.getLong(rowId) + case FloatType => col.getFloat(rowId) + case DoubleType => col.getDouble(rowId) + case d: DecimalType => col.getDecimal(rowId, d.precision, d.scale) + case StringType => col.getUTF8String(rowId).copy() + case _ => null + } + + // Compare values using the same physical representation used in the stats row. + private def compare(dt: DataType, left: Any, right: Any): Int = dt match { + case BooleanType => + java.lang.Boolean.compare(left.asInstanceOf[Boolean], right.asInstanceOf[Boolean]) + case ByteType => + java.lang.Byte.compare(left.asInstanceOf[Byte], right.asInstanceOf[Byte]) + case ShortType => + java.lang.Short.compare(left.asInstanceOf[Short], right.asInstanceOf[Short]) + case IntegerType | DateType => + java.lang.Integer.compare(left.asInstanceOf[Int], right.asInstanceOf[Int]) + case LongType | TimestampType | TimestampNTZType => + java.lang.Long.compare(left.asInstanceOf[Long], right.asInstanceOf[Long]) + case FloatType => + java.lang.Float.compare(left.asInstanceOf[Float], right.asInstanceOf[Float]) + case DoubleType => + java.lang.Double.compare(left.asInstanceOf[Double], right.asInstanceOf[Double]) + case _: DecimalType => + left.asInstanceOf[Decimal].compare(right.asInstanceOf[Decimal]) + case StringType => + ByteArray.compareBinary( + left.asInstanceOf[UTF8String].getBytes, + right.asInstanceOf[UTF8String].getBytes) + case other => + throw new IllegalStateException(s"compare called for unsupported type $other") + } + + // Compute Spark-compatible cache stats before serializing each batch to Arrow. + // The stats are stored beside the Arrow bytes so Spark's cache filter can prune + // CometCachedBatch without decoding the batch first. + // + // A columnar input batch is not guaranteed to be Arrow-backed; see supportsColumnarInput for + // why. Batches that are not get copied into Arrow first, since Utils.serializeBatches only + // writes CometVector columns. + private def encodeBatches( + batches: Iterator[ColumnarBatch], + attrs: Seq[Attribute]): Iterator[CachedBatch] = { + val arrowSchema = + Utils.toArrowSchema(Utils.fromAttributes(attrs), CometArrowStream.NATIVE_TIMEZONE) + + batches.map { batch => + // Bounds and null counts are read from the input batch, which serializing then clears, so + // they have to be gathered first. The row is only assembled once the per-column sizes are + // known. + val (lower, upper, nulls) = gatherColumnStats(batch, attrs) + val numRows = batch.numRows() + + val columns = if (Utils.isArrowBacked(batch)) { + Utils.serializeBatchColumns(batch) + } else { + val arrowBatch = + CometArrowConverters.columnarBatchToArrowBatch(batch, arrowSchema, CometArrowAllocator) + try Utils.serializeBatchColumns(arrowBatch) + finally arrowBatch.close() Review Comment: Filed the durable part of this as #5488. To be precise about what does and does not outlive this PR, since I called it pre-existing above and that was ambiguous. `CometInMemoryTableScanExec` and `ArrowCachedBatchSerializer` are both new files in #5051, so the two findings in the scan node cannot survive it being dropped. This one is different: `getFieldVector` and both `Utils.serializeBatches` consumers, `getByteArrayRdd` in `operators.scala` and `CometBroadcastExchangeExec`, are on `main` today. And the fix here does not close it. Making `isArrowBacked` reject those vectors only reroutes the cache write path to conversion; the broadcast and collect paths have no conversion fallback and still throw. `isSupportedFieldVector` is a useful building block for a real fix, but it goes away with this PR too. #5488 has the producer trail through `CometMapInBatchExec`, and notes that neither of us ran the mapInArrow path end to end. -- This is an automated message from the Apache Git Service. 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