add an overload of UpdateStateByKey API and move kafka DStream to KafkaUtil class
This commit is contained in:
renyi
Родитель
1b02544765
Коммит
669f6773f3
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@ -128,6 +128,7 @@
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<Compile Include="Sql\Types.cs" />
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<Compile Include="Sql\UserDefinedFunction.cs" />
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<Compile Include="Streaming\DStream.cs" />
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<Compile Include="Streaming\Kafka.cs" />
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<Compile Include="Streaming\PairDStreamFunctions.cs" />
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<Compile Include="Streaming\StreamingContext.cs" />
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<Compile Include="Streaming\TransformedDStream.cs" />
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@ -7,10 +7,75 @@ using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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using Microsoft.Spark.CSharp.Core;
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namespace Microsoft.Spark.CSharp.Streaming
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{
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//TODO - complete the impl
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public class Kafka
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public class KafkaUtils
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{
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/// <summary>
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/// Create an input stream that pulls messages from a Kafka Broker.
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/// </summary>
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/// <param name="zkQuorum">Zookeeper quorum (hostname:port,hostname:port,..).</param>
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/// <param name="groupId">The group id for this consumer.</param>
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/// <param name="topics">Dict of (topic_name -> numPartitions) to consume. Each partition is consumed in its own thread.</param>
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/// <param name="kafkaParams">Additional params for Kafka</param>
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/// <returns>A DStream object</returns>
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public static DStream<KeyValuePair<byte[], byte[]>> CreateStream(StreamingContext ssc, string zkQuorum, string groupId, Dictionary<string, int> topics, Dictionary<string, string> kafkaParams)
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{
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return CreateStream(ssc, zkQuorum, groupId, topics, kafkaParams, StorageLevelType.MEMORY_AND_DISK_SER_2);
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}
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/// <summary>
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/// Create an input stream that pulls messages from a Kafka Broker.
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/// </summary>
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/// <param name="zkQuorum">Zookeeper quorum (hostname:port,hostname:port,..).</param>
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/// <param name="groupId">The group id for this consumer.</param>
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/// <param name="topics">Dict of (topic_name -> numPartitions) to consume. Each partition is consumed in its own thread.</param>
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/// <param name="kafkaParams">Additional params for Kafka</param>
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/// <param name="storageLevelType">RDD storage level.</param>
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/// <returns>A DStream object</returns>
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public static DStream<KeyValuePair<byte[], byte[]>> CreateStream(StreamingContext ssc, string zkQuorum, string groupId, Dictionary<string, int> topics, Dictionary<string, string> kafkaParams, StorageLevelType storageLevelType)
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{
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if (kafkaParams == null)
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kafkaParams = new Dictionary<string, string>();
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if (!string.IsNullOrEmpty(zkQuorum))
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kafkaParams["zookeeper.connect"] = zkQuorum;
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if (groupId != null)
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kafkaParams["group.id"] = groupId;
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if (kafkaParams.ContainsKey("zookeeper.connection.timeout.ms"))
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kafkaParams["zookeeper.connection.timeout.ms"] = "10000";
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return new DStream<KeyValuePair<byte[], byte[]>>(ssc.streamingContextProxy.KafkaStream(topics, kafkaParams, storageLevelType), ssc);
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}
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/// <summary>
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/// Create an input stream that directly pulls messages from a Kafka Broker and specific offset.
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///
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/// This is not a receiver based Kafka input stream, it directly pulls the message from Kafka
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/// in each batch duration and processed without storing.
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///
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/// This does not use Zookeeper to store offsets. The consumed offsets are tracked
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/// by the stream itself. For interoperability with Kafka monitoring tools that depend on
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/// Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
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/// You can access the offsets used in each batch from the generated RDDs (see
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/// [[org.apache.spark.streaming.kafka.HasOffsetRanges]]).
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/// To recover from driver failures, you have to enable checkpointing in the StreamingContext.
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/// The information on consumed offset can be recovered from the checkpoint.
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/// See the programming guide for details (constraints, etc.).
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///
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/// </summary>
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/// <param name="topics">list of topic_name to consume.</param>
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/// <param name="kafkaParams">
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/// Additional params for Kafka. Requires "metadata.broker.list" or "bootstrap.servers" to be set
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/// with Kafka broker(s) (NOT zookeeper servers), specified in host1:port1,host2:port2 form.
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/// </param>
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/// <param name="fromOffsets">Per-topic/partition Kafka offsets defining the (inclusive) starting point of the stream.</param>
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/// <returns>A DStream object</returns>
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public static DStream<KeyValuePair<byte[], byte[]>> CreateDirectStream(StreamingContext ssc, List<string> topics, Dictionary<string, string> kafkaParams, Dictionary<string, long> fromOffsets)
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{
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return new DStream<KeyValuePair<byte[], byte[]>>(ssc.streamingContextProxy.DirectKafkaStream(topics, kafkaParams, fromOffsets), ssc, SerializedMode.Pair);
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}
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}
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}
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@ -315,19 +315,37 @@ namespace Microsoft.Spark.CSharp.Streaming
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/// <typeparam name="V"></typeparam>
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/// <typeparam name="S"></typeparam>
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/// <param name="self"></param>
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/// <param name="updateFunc">State update function. If this function returns None, then corresponding state key-value pair will be eliminated.</param>
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/// <param name="updateFunc"></param>
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/// <param name="numPartitions"></param>
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/// <returns></returns>
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public static DStream<KeyValuePair<K, S>> UpdateStateByKey<K, V, S>(this DStream<KeyValuePair<K, V>> self,
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Func<IEnumerable<V>, S, S> updateFunc,
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int numPartitions = 0)
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{
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return UpdateStateByKey<K, V, S>(self, new UpdateStateByKeyHelper<K, V, S>(updateFunc).Execute, numPartitions);
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}
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/// <summary>
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/// Return a new "state" DStream where the state for each key is updated by applying
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/// the given function on the previous state of the key and the new values of the key.
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/// </summary>
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/// <typeparam name="K"></typeparam>
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/// <typeparam name="V"></typeparam>
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/// <typeparam name="S"></typeparam>
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/// <param name="self"></param>
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/// <param name="updateFunc">State update function. If this function returns None, then corresponding state key-value pair will be eliminated.</param>
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/// <param name="numPartitions"></param>
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/// <returns></returns>
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public static DStream<KeyValuePair<K, S>> UpdateStateByKey<K, V, S>(this DStream<KeyValuePair<K, V>> self,
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Func<IEnumerable<KeyValuePair<K, Tuple<IEnumerable<V>, S>>>, IEnumerable<KeyValuePair<K, S>>> updateFunc,
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int numPartitions = 0)
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{
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if (numPartitions <= 0)
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numPartitions = self.streamingContext.SparkContext.DefaultParallelism;
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Func<double, RDD<dynamic>, RDD<dynamic>> prevFunc = self.Piplinable ? (self as TransformedDStream<KeyValuePair<K, V>>).func : null;
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Func<double, RDD<dynamic>, RDD<dynamic>, RDD<dynamic>> func = new UpdateStateByKeyHelper<K, V, S>(updateFunc, prevFunc, numPartitions).Execute;
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Func<double, RDD<dynamic>, RDD<dynamic>, RDD<dynamic>> func = new UpdateStateByKeysHelper<K, V, S>(updateFunc, prevFunc, numPartitions).Execute;
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var formatter = new BinaryFormatter();
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var stream = new MemoryStream();
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@ -565,9 +583,27 @@ namespace Microsoft.Spark.CSharp.Streaming
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internal class UpdateStateByKeyHelper<K, V, S>
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{
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private readonly Func<IEnumerable<V>, S, S> func;
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internal UpdateStateByKeyHelper(Func<IEnumerable<V>, S, S> f)
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{
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func = f;
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}
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internal IEnumerable<KeyValuePair<K, S>> Execute(IEnumerable<KeyValuePair<K, Tuple<IEnumerable<V>, S>>> input)
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{
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return input.Select(x => new KeyValuePair<K, S>(x.Key, func(x.Value.Item1, x.Value.Item2)));
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}
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}
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[Serializable]
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internal class UpdateStateByKeysHelper<K, V, S>
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{
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private readonly Func<IEnumerable<KeyValuePair<K, Tuple<IEnumerable<V>, S>>>, IEnumerable<KeyValuePair<K, S>>> func;
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private readonly Func<double, RDD<dynamic>, RDD<dynamic>> prevFunc;
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private readonly int numPartitions;
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internal UpdateStateByKeyHelper(Func<IEnumerable<V>, S, S> f, Func<double, RDD<dynamic>, RDD<dynamic>> prevF, int numPartitions)
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internal UpdateStateByKeysHelper(
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Func<IEnumerable<KeyValuePair<K, Tuple<IEnumerable<V>, S>>>, IEnumerable<KeyValuePair<K, S>>> f,
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Func<double, RDD<dynamic>, RDD<dynamic>> prevF, int numPartitions)
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{
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func = f;
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prevFunc = prevF;
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@ -577,7 +613,7 @@ namespace Microsoft.Spark.CSharp.Streaming
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internal RDD<dynamic> Execute(double t, RDD<dynamic> stateRDD, RDD<dynamic> valuesRDD)
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{
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RDD<KeyValuePair<K, S>> state = null;
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RDD<KeyValuePair<K, Tuple<List<V>, S>>> g = null;
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RDD<KeyValuePair<K, Tuple<IEnumerable<V>, S>>> g = null;
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if (prevFunc != null)
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valuesRDD = prevFunc(t, valuesRDD);
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@ -586,17 +622,17 @@ namespace Microsoft.Spark.CSharp.Streaming
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if (stateRDD == null)
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{
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g = values.GroupByKey(numPartitions).MapValues(x => new Tuple<List<V>, S>(new List<V>(x), default(S)));
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g = values.GroupByKey(numPartitions).MapValues(x => new Tuple<IEnumerable<V>, S>(new List<V>(x), default(S)));
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}
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else
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{
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state = stateRDD.ConvertTo<KeyValuePair<K, S>>();
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values = values.PartitionBy(numPartitions);
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state.partitioner = values.partitioner;
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g = state.GroupWith(values, numPartitions).MapValues(x => new Tuple<List<V>, S>(new List<V>(x.Item2), x.Item1.Count > 0 ? x.Item1[0] : default(S)));
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g = state.GroupWith(values, numPartitions).MapValues(x => new Tuple<IEnumerable<V>, S>(new List<V>(x.Item2), x.Item1.Count > 0 ? x.Item1[0] : default(S)));
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}
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state = g.MapValues(x => func(x.Item1, x.Item2)).Filter(x => x.Value != null);
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state = g.MapPartitionsWithIndex((pid, iter) => func(iter), true).Filter(x => x.Value != null);
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return state.ConvertTo<dynamic>();
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}
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@ -141,71 +141,6 @@ namespace Microsoft.Spark.CSharp.Streaming
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return new DStream<string>(streamingContextProxy.TextFileStream(directory), this, SerializedMode.String);
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}
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/// <summary>
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/// Create an input stream that pulls messages from a Kafka Broker.
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/// </summary>
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/// <param name="zkQuorum">Zookeeper quorum (hostname:port,hostname:port,..).</param>
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/// <param name="groupId">The group id for this consumer.</param>
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/// <param name="topics">Dict of (topic_name -> numPartitions) to consume. Each partition is consumed in its own thread.</param>
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/// <param name="kafkaParams">Additional params for Kafka</param>
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/// <returns>A DStream object</returns>
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public DStream<KeyValuePair<byte[], byte[]>> KafkaStream(string zkQuorum, string groupId, Dictionary<string, int> topics, Dictionary<string, string> kafkaParams)
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{
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return this.KafkaStream(zkQuorum, groupId, topics, kafkaParams, StorageLevelType.MEMORY_AND_DISK_SER_2);
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}
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/// <summary>
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/// Create an input stream that pulls messages from a Kafka Broker.
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/// </summary>
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/// <param name="zkQuorum">Zookeeper quorum (hostname:port,hostname:port,..).</param>
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/// <param name="groupId">The group id for this consumer.</param>
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/// <param name="topics">Dict of (topic_name -> numPartitions) to consume. Each partition is consumed in its own thread.</param>
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/// <param name="kafkaParams">Additional params for Kafka</param>
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/// <param name="storageLevelType">RDD storage level.</param>
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/// <returns>A DStream object</returns>
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public DStream<KeyValuePair<byte[], byte[]>> KafkaStream(string zkQuorum, string groupId, Dictionary<string, int> topics, Dictionary<string, string> kafkaParams, StorageLevelType storageLevelType)
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{
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if (kafkaParams == null)
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kafkaParams = new Dictionary<string, string>();
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if (!string.IsNullOrEmpty(zkQuorum))
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kafkaParams["zookeeper.connect"] = zkQuorum;
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if (groupId != null)
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kafkaParams["group.id"] = groupId;
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if (kafkaParams.ContainsKey("zookeeper.connection.timeout.ms"))
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kafkaParams["zookeeper.connection.timeout.ms"] = "10000";
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return new DStream<KeyValuePair<byte[], byte[]>>(this.streamingContextProxy.KafkaStream(topics, kafkaParams, storageLevelType), this);
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}
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/// <summary>
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/// Create an input stream that directly pulls messages from a Kafka Broker and specific offset.
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///
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/// This is not a receiver based Kafka input stream, it directly pulls the message from Kafka
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/// in each batch duration and processed without storing.
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///
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/// This does not use Zookeeper to store offsets. The consumed offsets are tracked
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/// by the stream itself. For interoperability with Kafka monitoring tools that depend on
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/// Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
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/// You can access the offsets used in each batch from the generated RDDs (see
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///
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/// To recover from driver failures, you have to enable checkpointing in the StreamingContext.
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/// The information on consumed offset can be recovered from the checkpoint.
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/// See the programming guide for details (constraints, etc.).
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///
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/// </summary>
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/// <param name="topics">list of topic_name to consume.</param>
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/// <param name="kafkaParams">
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/// Additional params for Kafka. Requires "metadata.broker.list" or "bootstrap.servers" to be set
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/// with Kafka broker(s) (NOT zookeeper servers), specified in host1:port1,host2:port2 form.
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/// </param>
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/// <param name="fromOffsets">Per-topic/partition Kafka offsets defining the (inclusive) starting point of the stream.</param>
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/// <returns>A DStream object</returns>
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public DStream<KeyValuePair<byte[], byte[]>> DirectKafkaStream(List<string> topics, Dictionary<string, string> kafkaParams, Dictionary<string, long> fromOffsets)
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{
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return new DStream<KeyValuePair<byte[], byte[]>>(this.streamingContextProxy.DirectKafkaStream(topics, kafkaParams, fromOffsets), this, SerializedMode.Pair);
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}
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/// <summary>
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/// Wait for the execution to stop.
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/// </summary>
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@ -31,10 +31,14 @@ namespace AdapterTest
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var socketStream = ssc.SocketTextStream(IPAddress.Loopback.ToString(), 12345);
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Assert.IsNotNull(socketStream.DStreamProxy);
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<<<<<<< 1b025447650d8c2a36b151dfb9e1b5ed5039e5f7
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var kafkaStream = ssc.KafkaStream(IPAddress.Loopback + ":2181", "testGroupId", new Dictionary<string, int> { { "testTopic1", 1 } }, new Dictionary<string, string>());
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=======
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var kafkaStream = KafkaUtils.CreateStream(ssc, "127.0.0.1:2181", "testGroupId", new Dictionary<string, int> { { "testTopic1", 1 } }, new Dictionary<string, string>());
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>>>>>>> add an overload of UpdateStateByKey API and move kafka DStream to KafkaUtil class
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Assert.IsNotNull(kafkaStream.DStreamProxy);
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var directKafkaStream = ssc.DirectKafkaStream(new List<string> { "testTopic2" }, new Dictionary<string, string>(), new Dictionary<string, long>());
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var directKafkaStream = KafkaUtils.CreateDirectStream(ssc, new List<string> { "testTopic2" }, new Dictionary<string, string>(), new Dictionary<string, long>());
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Assert.IsNotNull(directKafkaStream.DStreamProxy);
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var union = ssc.Union(textFile, socketStream);
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