neilramaswamy commented on code in PR #48853:
URL: https://github.com/apache/spark/pull/48853#discussion_r1847353680
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TTLState.scala:
##########
@@ -19,274 +19,553 @@ package org.apache.spark.sql.execution.streaming
import java.time.Duration
import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
-import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, UnsafeRow}
+import org.apache.spark.sql.catalyst.expressions.{GenericInternalRow,
UnsafeProjection, UnsafeRow}
+import org.apache.spark.sql.execution.metric.SQLMetric
import
org.apache.spark.sql.execution.streaming.TransformWithStateKeyValueRowSchemaUtils._
-import
org.apache.spark.sql.execution.streaming.state.{RangeKeyScanStateEncoderSpec,
StateStore}
+import
org.apache.spark.sql.execution.streaming.state.{NoPrefixKeyStateEncoderSpec,
RangeKeyScanStateEncoderSpec, StateStore}
+import org.apache.spark.sql.streaming.TTLConfig
import org.apache.spark.sql.types._
-object StateTTLSchema {
- val TTL_VALUE_ROW_SCHEMA: StructType =
- StructType(Array(StructField("__dummy__", NullType)))
-}
-
/**
- * Encapsulates the ttl row information stored in [[SingleKeyTTLStateImpl]].
+ * Any state variable that wants to support TTL must implement this trait,
+ * which they can do by extending [[OneToOneTTLState]] or
[[OneToManyTTLState]].
*
- * @param groupingKey grouping key for which ttl is set
- * @param expirationMs expiration time for the grouping key
- */
-case class SingleKeyTTLRow(
- groupingKey: UnsafeRow,
- expirationMs: Long)
-
-/**
- * Encapsulates the ttl row information stored in [[CompositeKeyTTLStateImpl]].
+ * The only required methods here are ones relating to evicting expired and all
+ * state, via clearExpiredStateForAllKeys and clearAllStateForElementKey,
+ * respectively. How classes do this is implementation detail, but the general
+ * pattern is to use secondary indexes to make sure cleanup scans
+ * theta(records to evict), not theta(all records).
*
- * @param groupingKey grouping key for which ttl is set
- * @param userKey user key for which ttl is set
- * @param expirationMs expiration time for the grouping key
- */
-case class CompositeKeyTTLRow(
- groupingKey: UnsafeRow,
- userKey: UnsafeRow,
- expirationMs: Long)
-
-/**
- * Represents the underlying state for secondary TTL Index for a user defined
- * state variable.
+ * There are two broad patterns of implementing stateful variables, and thus
+ * there are two broad patterns for implementing TTL. The first is when there
+ * is a one-to-one mapping between an element key [1] and a value; the primary
+ * and secondary index management for this case is implemented by
+ * [[OneToOneTTLState]]. When a single element key can have multiple values,
+ * all of which can expire at their own, unique times, then
+ * [[OneToManyTTLState]] should be used.
+ *
+ * In either case, implementations need to use some sort of secondary index
+ * that orders element keys by expiration time. This base functionality
+ * is provided by methods in this trait that read/write/delete to the
+ * so-called "TTL index". It is a secondary index with the layout of
+ * (expirationMs, elementKey) -> EMPTY_ROW. The expirationMs is big-endian
+ * encoded to allow for efficient range scans to find all expired keys.
+ *
+ * TTLState (or any abstract sub-classes) should never deal with encoding or
+ * decoding UnsafeRows to and from their user-facing types. The stateful
variable
+ * themselves should be doing this; all other TTLState sub-classes should be
concerned
+ * only with writing, reading, and deleting UnsafeRows and their associated
+ * expirations from the primary and secondary indexes. [2]
*
- * This state allows Spark to query ttl values based on expiration time
- * allowing efficient ttl cleanup.
+ * [1]. You might ask, why call it "element key" instead of "grouping key"?
+ * This is because a single grouping key might have multiple elements, as
in
+ * the case of a map, which has composite keys of the form (groupingKey,
mapKey).
+ * In the case of ValueState, though, the element key is the grouping key.
+ * To generalize to both cases, this class should always use the term
elementKey.)
+ *
+ * [2]. You might also ask, why design it this way? We want the TTLState
abstract
+ * sub-classes to write to both the primary and secondary indexes, since
they
+ * both need to stay in sync; co-locating the logic is cleanest.
*/
trait TTLState {
+ // Name of the state variable, e.g. the string the user passes to
get{Value/List/Map}State
+ // in the init() method of a StatefulProcessor.
+ def stateName: String
+
+ // The StateStore instance used to store the state. There is only one
instance shared
+ // among the primary and secondary indexes, since it uses virtual column
families
+ // to keep the indexes separate.
+ def store: StateStore
+
+ // The schema of the primary key for the state variable. For value and list
state, this
+ // is the grouping key. For map state, this is the composite key of the
grouping key and
+ // a map key.
+ def elementKeySchema: StructType
+
+ // The timestamp at which the batch is being processed. All state variables
that have
+ // an expiration at or before this timestamp must be cleaned up.
+ def batchTimestampMs: Long
+
+ // The configuration for this run of the streaming query. It may change
between runs
+ // (e.g. user sets ttlConfig1, stops their query, updates to ttlConfig2, and
then
+ // resumes their query).
+ def ttlConfig: TTLConfig
+
+ // A map from metric name to the underlying SQLMetric. This should not be
updated
+ // by the underlying state variable, as the TTL state implementation should
be
+ // handling all reads/writes/deletes to the indexes.
+ def metrics: Map[String, SQLMetric] = Map.empty
+
+ private final val TTL_INDEX = "$ttl_" + stateName
+ private final val TTL_INDEX_KEY_SCHEMA =
getSingleKeyTTLRowSchema(elementKeySchema)
+ private final val TTL_EMPTY_VALUE_ROW_SCHEMA: StructType =
+ StructType(Array(StructField("__empty__", NullType)))
+
+ private final val TTL_ENCODER = new TTLEncoder(elementKeySchema)
+
+ // Empty row used for values
+ private final val TTL_EMPTY_VALUE_ROW =
+
UnsafeProjection.create(Array[DataType](NullType)).apply(InternalRow.apply(null))
+
+ protected final def ttlExpirationMs = StateTTL
+ .calculateExpirationTimeForDuration(ttlConfig.ttlDuration,
batchTimestampMs)
+
+ store.createColFamilyIfAbsent(
+ TTL_INDEX,
+ TTL_INDEX_KEY_SCHEMA,
+ TTL_EMPTY_VALUE_ROW_SCHEMA,
+ RangeKeyScanStateEncoderSpec(TTL_INDEX_KEY_SCHEMA, Seq(0)),
+ isInternal = true
+ )
+
+ protected def insertIntoTTLIndex(expirationMs: Long, elementKey: UnsafeRow):
Unit = {
+ val secondaryIndexKey = TTL_ENCODER.encodeTTLRow(expirationMs, elementKey)
+ store.put(secondaryIndexKey, TTL_EMPTY_VALUE_ROW, TTL_INDEX)
+ }
+
+ protected def deleteFromTTLIndex(expirationMs: Long, elementKey: UnsafeRow):
Unit = {
+ val secondaryIndexKey = TTL_ENCODER.encodeTTLRow(expirationMs, elementKey)
+ store.remove(secondaryIndexKey, TTL_INDEX)
+ }
+
+ private[sql] def toTTLRow(ttlKey: UnsafeRow): TTLRow = {
+ val expirationMs = ttlKey.getLong(0)
+ val elementKey = ttlKey.getStruct(1, TTL_INDEX_KEY_SCHEMA.length)
+ TTLRow(elementKey, expirationMs)
+ }
+
+ private[sql] def getTTLRows(): Iterator[TTLRow] = {
+ store.iterator(TTL_INDEX).map(kv => toTTLRow(kv.key))
+ }
+
+ protected def ttlEvictionIterator(): Iterator[TTLRow] = {
Review Comment:
note to self: consider _not_ removing in the iterator
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