SparkCLR (pronounced Sparkler) adds C# language binding to Apache Spark, enabling the implementation of Spark driver code and data processing operations in C#.
For example, the word count sample in Apache Spark can be implemented in C# as follows :
var lines = sparkContext.TextFile(@"hdfs://path/to/input.txt");
var words = lines.FlatMap(s => s.Split(' '));
var wordCounts = words.Map(w => new KeyValuePair<string, int>(w.Trim(), 1))
.ReduceByKey((x, y) => x + y);
var wordCountCollection = wordCounts.Collect();
wordCounts.SaveAsTextFile(@"hdfs://path/to/wordcount.txt"); A simple DataFrame application using TempTable may look like the following:
var reqDataFrame = sqlContext.TextFile(@"hdfs://path/to/requests.csv");
var metricDataFrame = sqlContext.TextFile(@"hdfs://path/to/metrics.csv");
reqDataFrame.RegisterTempTable("requests");
metricDataFrame.RegisterTempTable("metrics");
// C0 - guid in requests DataFrame, C3 - guid in metrics DataFrame
var joinDataFrame = GetSqlContext().Sql(
"SELECT joinedtable.datacenter" +
", MAX(joinedtable.latency) maxlatency" +
", AVG(joinedtable.latency) avglatency " +
"FROM (" +
"SELECT a.C1 as datacenter, b.C6 as latency " +
"FROM requests a JOIN metrics b ON a.C0 = b.C3) joinedtable " +
"GROUP BY datacenter");
joinDataFrame.ShowSchema();
joinDataFrame.Show();A simple DataFrame application using DataFrame DSL may look like the following:
// C0 - guid, C1 - datacenter
var reqDataFrame = sqlContext.TextFile(@"hdfs://path/to/requests.csv")
.Select("C0", "C1");
// C3 - guid, C6 - latency
var metricDataFrame = sqlContext.TextFile(@"hdfs://path/to/metrics.csv", ",", false, true)
.Select("C3", "C6"); //override delimiter, hasHeader & inferSchema
var joinDataFrame = reqDataFrame.Join(metricDataFrame, reqDataFrame["C0"] == metricDataFrame["C3"])
.GroupBy("C1");
var maxLatencyByDcDataFrame = joinDataFrame.Agg(new Dictionary<string, string> { { "C6", "max" } });
maxLatencyByDcDataFrame.ShowSchema();
maxLatencyByDcDataFrame.Show();Refer to SparkCLR\csharp\Samples directory and sample usage for complete samples.
Refer to SparkCLR C# API documentation for the list of Spark's data processing operations supported in SparkCLR.
Refer to the docs folder for design overview and other info on SparkCLR
| Ubuntu 14.04.3 LTS | Windows |
|---|---|
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(Note: Tested with Spark 1.5.2)
SparkCLR is licensed under the MIT license. See LICENSE file for full license information.
We welcome contributions. To contribute, follow the instructions in CONTRIBUTING.md.