diff --git a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala index ea8dcdfd5d7d9..f346cf8d65806 100644 --- a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala +++ b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala @@ -38,7 +38,7 @@ import org.apache.spark.util._ * callbacks are protected by locking on the context instance. For instance, this ensures * that you cannot add a completion listener in one thread while we are completing (and calling * the completion listeners) in another thread. Other state is immutable, however the exposed - * [[TaskMetrics]] & [[MetricsSystem]] objects are not thread safe. + * `TaskMetrics` & `MetricsSystem` objects are not thread safe. */ private[spark] class TaskContextImpl( val stageId: Int, diff --git a/mllib/src/main/scala/org/apache/spark/ml/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/ml/fpm/FPGrowth.scala index e2bc270b38da7..65cc80619569e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/fpm/FPGrowth.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/fpm/FPGrowth.scala @@ -69,8 +69,8 @@ private[fpm] trait FPGrowthParams extends Params with HasPredictionCol { def getMinSupport: Double = $(minSupport) /** - * Number of partitions (>=1) used by parallel FP-growth. By default the param is not set, and - * partition number of the input dataset is used. + * Number of partitions (at least 1) used by parallel FP-growth. By default the param is not + * set, and partition number of the input dataset is used. * @group expertParam */ @Since("2.2.0") diff --git a/sql/core/src/main/java/org/apache/spark/api/java/function/FlatMapGroupsWithStateFunction.java b/sql/core/src/main/java/org/apache/spark/api/java/function/FlatMapGroupsWithStateFunction.java index 026b37cabbf1c..802949c0ddb60 100644 --- a/sql/core/src/main/java/org/apache/spark/api/java/function/FlatMapGroupsWithStateFunction.java +++ b/sql/core/src/main/java/org/apache/spark/api/java/function/FlatMapGroupsWithStateFunction.java @@ -27,7 +27,7 @@ /** * ::Experimental:: * Base interface for a map function used in - * {@link org.apache.spark.sql.KeyValueGroupedDataset#flatMapGroupsWithState( + * {@code org.apache.spark.sql.KeyValueGroupedDataset.flatMapGroupsWithState( * FlatMapGroupsWithStateFunction, org.apache.spark.sql.streaming.OutputMode, * org.apache.spark.sql.Encoder, org.apache.spark.sql.Encoder)} * @since 2.1.1 diff --git a/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala b/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala index 87c5621768872..022c2f5629e86 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala @@ -298,7 +298,7 @@ class KeyValueGroupedDataset[K, V] private[sql]( * For a static batch Dataset, the function will be invoked once per group. For a streaming * Dataset, the function will be invoked for each group repeatedly in every trigger, and * updates to each group's state will be saved across invocations. - * See [[GroupState]] for more details. + * See `GroupState` for more details. * * @tparam S The type of the user-defined state. Must be encodable to Spark SQL types. * @tparam U The type of the output objects. Must be encodable to Spark SQL types. @@ -328,7 +328,7 @@ class KeyValueGroupedDataset[K, V] private[sql]( * For a static batch Dataset, the function will be invoked once per group. For a streaming * Dataset, the function will be invoked for each group repeatedly in every trigger, and * updates to each group's state will be saved across invocations. - * See [[GroupState]] for more details. + * See `GroupState` for more details. * * @tparam S The type of the user-defined state. Must be encodable to Spark SQL types. * @tparam U The type of the output objects. Must be encodable to Spark SQL types. @@ -360,7 +360,7 @@ class KeyValueGroupedDataset[K, V] private[sql]( * For a static batch Dataset, the function will be invoked once per group. For a streaming * Dataset, the function will be invoked for each group repeatedly in every trigger, and * updates to each group's state will be saved across invocations. - * See [[GroupState]] for more details. + * See `GroupState` for more details. * * @tparam S The type of the user-defined state. Must be encodable to Spark SQL types. * @tparam U The type of the output objects. Must be encodable to Spark SQL types. @@ -400,7 +400,7 @@ class KeyValueGroupedDataset[K, V] private[sql]( * For a static batch Dataset, the function will be invoked once per group. For a streaming * Dataset, the function will be invoked for each group repeatedly in every trigger, and * updates to each group's state will be saved across invocations. - * See [[GroupState]] for more details. + * See `GroupState` for more details. * * @tparam S The type of the user-defined state. Must be encodable to Spark SQL types. * @tparam U The type of the output objects. Must be encodable to Spark SQL types. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/streaming/GroupState.scala b/sql/core/src/main/scala/org/apache/spark/sql/streaming/GroupState.scala index 60a4d0d8f98a1..15df906ca7b13 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/streaming/GroupState.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/streaming/GroupState.scala @@ -18,18 +18,18 @@ package org.apache.spark.sql.streaming import org.apache.spark.annotation.{Experimental, InterfaceStability} -import org.apache.spark.sql.{Encoder, KeyValueGroupedDataset} +import org.apache.spark.sql.KeyValueGroupedDataset import org.apache.spark.sql.catalyst.plans.logical.LogicalGroupState /** * :: Experimental :: * * Wrapper class for interacting with per-group state data in `mapGroupsWithState` and - * `flatMapGroupsWithState` operations on [[KeyValueGroupedDataset]]. + * `flatMapGroupsWithState` operations on `KeyValueGroupedDataset`. * * Detail description on `[map/flatMap]GroupsWithState` operation * -------------------------------------------------------------- - * Both, `mapGroupsWithState` and `flatMapGroupsWithState` in [[KeyValueGroupedDataset]] + * Both, `mapGroupsWithState` and `flatMapGroupsWithState` in `KeyValueGroupedDataset` * will invoke the user-given function on each group (defined by the grouping function in * `Dataset.groupByKey()`) while maintaining user-defined per-group state between invocations. * For a static batch Dataset, the function will be invoked once per group. For a streaming @@ -70,8 +70,8 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalGroupState * `[map|flatMap]GroupsWithState`, but the exact timeout duration/timestamp is configurable per * group by calling `setTimeout...()` in `GroupState`. * - Timeouts can be either based on processing time (i.e. - * [[GroupStateTimeout.ProcessingTimeTimeout]]) or event time (i.e. - * [[GroupStateTimeout.EventTimeTimeout]]). + * `GroupStateTimeout.ProcessingTimeTimeout`) or event time (i.e. + * `GroupStateTimeout.EventTimeTimeout`). * - With `ProcessingTimeTimeout`, the timeout duration can be set by calling * `GroupState.setTimeoutDuration`. The timeout will occur when the clock has advanced by the set * duration. Guarantees provided by this timeout with a duration of D ms are as follows: @@ -177,7 +177,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalGroupState * }}} * * @tparam S User-defined type of the state to be stored for each group. Must be encodable into - * Spark SQL types (see [[Encoder]] for more details). + * Spark SQL types (see `Encoder` for more details). * @since 2.2.0 */ @Experimental @@ -224,7 +224,7 @@ trait GroupState[S] extends LogicalGroupState[S] { /** * Set the timeout duration for this key as a string. For example, "1 hour", "2 days", etc. * - * @note, ProcessingTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. + * @note ProcessingTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. */ @throws[IllegalArgumentException]("if 'duration' is not a valid duration") @throws[IllegalStateException]("when state is either not initialized, or already removed") @@ -240,7 +240,7 @@ trait GroupState[S] extends LogicalGroupState[S] { * Set the timeout timestamp for this key as milliseconds in epoch time. * This timestamp cannot be older than the current watermark. * - * @note, EventTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. + * @note EventTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. */ def setTimeoutTimestamp(timestampMs: Long): Unit @@ -254,7 +254,7 @@ trait GroupState[S] extends LogicalGroupState[S] { * The final timestamp (including the additional duration) cannot be older than the * current watermark. * - * @note, EventTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. + * @note EventTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. */ def setTimeoutTimestamp(timestampMs: Long, additionalDuration: String): Unit @@ -265,7 +265,7 @@ trait GroupState[S] extends LogicalGroupState[S] { * Set the timeout timestamp for this key as a java.sql.Date. * This timestamp cannot be older than the current watermark. * - * @note, EventTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. + * @note EventTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. */ def setTimeoutTimestamp(timestamp: java.sql.Date): Unit @@ -279,7 +279,7 @@ trait GroupState[S] extends LogicalGroupState[S] { * The final timestamp (including the additional duration) cannot be older than the * current watermark. * - * @note, EventTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. + * @note EventTimeTimeout must be enabled in `[map/flatmap]GroupsWithStates`. */ def setTimeoutTimestamp(timestamp: java.sql.Date, additionalDuration: String): Unit }