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![Spark DataFrames with SparkSQL works with Spark v2.3.x/v2.4.[0/1] and includes ~300 SparkSQL functions .NET Spark UDFs Batch & streaming including Spark Structured Streaming and all Spark-supported data sources .NET Standard 2.0 works with .NET Framework v4.6.1+ and .NET Core v2.1+ and includes C#/F# support .NET Standard Machine Learning Including access to ML.NET Speed & productivity Performance optimized interop, as fast or faster than pySpark https://github.com/dotnet/spark/examples](https://image.slidesharecdn.com/brk3055rysv05-190508194339/75/Building-data-pipelines-for-modern-data-warehouse-with-Apache-Spark-and-NET-in-Azure-Microsoft-Build-2019-15-2048.jpg)
![var spark = SparkSession.Builder().GetOrCreate(); var dataframe = spark.Read().Json(“input.json”); dataframe.Filter(df["age"] > 21) .Select(concat(df[“age”], df[“name”]).Show(); var concat = Udf<int?, string, string>((age, name)=>name+age);](https://image.slidesharecdn.com/brk3055rysv05-190508194339/75/Building-data-pipelines-for-modern-data-warehouse-with-Apache-Spark-and-NET-in-Azure-Microsoft-Build-2019-16-2048.jpg)
![val europe = region.filter($"r_name" === "EUROPE") .join(nation, $"r_regionkey" === nation("n_regionkey")) .join(supplier, $"n_nationkey" === supplier("s_nationkey")) .join(partsupp, supplier("s_suppkey") === partsupp("ps_suppkey")) val brass = part.filter(part("p_size") === 15 && part("p_type").endsWith("BRASS")) .join(europe, europe("ps_partkey") === $"p_partkey") val minCost = brass.groupBy(brass("ps_partkey")) .agg(min("ps_supplycost").as("min")) brass.join(minCost, brass("ps_partkey") === minCost("ps_partkey")) .filter(brass("ps_supplycost") === minCost("min")) .select("s_acctbal", "s_name", "n_name", "p_partkey", "p_mfgr", "s_address", "s_phone", "s_comment") .sort($"s_acctbal".desc, $"n_name", $"s_name", $"p_partkey") .limit(100) .show() var europe = region.Filter(Col("r_name") == "EUROPE") .Join(nation, Col("r_regionkey") == nation["n_regionkey"]) .Join(supplier, Col("n_nationkey") == supplier["s_nationkey"]) .Join(partsupp, supplier["s_suppkey"] == partsupp["ps_suppkey"]); var brass = part.Filter(part["p_size"] == 15 & part["p_type"].EndsWith("BRASS")) .Join(europe, europe["ps_partkey"] == Col("p_partkey")); var minCost = brass.GroupBy(brass["ps_partkey"]) .Agg(Min("ps_supplycost").As("min")); brass.Join(minCost, brass["ps_partkey"] == minCost["ps_partkey"]) .Filter(brass["ps_supplycost"] == minCost["min"]) .Select("s_acctbal", "s_name", "n_name", "p_partkey", "p_mfgr", "s_address", "s_phone", "s_comment") .Sort(Col("s_acctbal").Desc(), Col("n_name"), Col("s_name"), Col("p_partkey")) .Limit(100) .Show(); Similar syntax – dangerously copy/paste friendly! $”col_name” vs. Col(“col_name”) Capitalization Scala C# C# vs Scala (e.g., == vs ===)](https://image.slidesharecdn.com/brk3055rysv05-190508194339/75/Building-data-pipelines-for-modern-data-warehouse-with-Apache-Spark-and-NET-in-Azure-Microsoft-Build-2019-17-2048.jpg)
![CREATE EXTERNAL DATA SOURCE MyADLSGen2 WITH (TYPE = Hadoop, LOCATION = ‘abfs://<filesys>@<account_name>.dfs.core.windows.net’, CREDENTIAL = <Database scoped credential>); CREATE EXTERNAL FILE FORMAT ParquetFile WITH ( FORMAT_TYPE = PARQUET, DATA_COMPRESSION = 'org.apache.hadoop.io.compress.GzipCodec’, FORMAT_OPTIONS (FIELD_TERMINATOR ='|', USE_TYPE_DEFAULT = TRUE)); CREATE EXTERNAL TABLE [dbo].[Customer_import] ( [SensorKey] int NOT NULL, int NOT NULL, [Speed] float NOT NULL) WITH (LOCATION=‘/Dimensions/customer', DATA_SOURCE = MyADLSGen2, FILE_FORMAT = ParquetFile) Once per store account (WASB, ADLS G1, ADLS G2) Once per file format, supports Parquet (snappy or Gzip), ORC, RC, CSV/TSV Folder path](https://image.slidesharecdn.com/brk3055rysv05-190508194339/75/Building-data-pipelines-for-modern-data-warehouse-with-Apache-Spark-and-NET-in-Azure-Microsoft-Build-2019-29-2048.jpg)
![CREATE TABLE [dbo].[Customer] WITH ( DISTRIBUTION = ROUND_ROBIN , CLUSTERED INDEX (customerid) ) AS SELECT * FROM [dbo].[Customer_import] INSERT INTO [dbo].[Customer] SELECT * FROM [dbo].[Customer_import] WHERE <predicate to determine new data>](https://image.slidesharecdn.com/brk3055rysv05-190508194339/75/Building-data-pipelines-for-modern-data-warehouse-with-Apache-Spark-and-NET-in-Azure-Microsoft-Build-2019-30-2048.jpg)
Uploaded byMichael Rys
Building data pipelines for modern data warehouse with Apache® Spark™ and .NET in Azure (Microsoft Build 2019)
The document discusses the integration of .NET with Apache Spark to enhance data processing capabilities, particularly for .NET developers interested in big data applications. It highlights the benefits of modern data warehousing, advanced analytics, and real-time insights, while also outlining the support and interoperability for .NET within the Spark ecosystem. Additionally, it covers architecture components, programming experiences, performance optimizations, and the commitment of Microsoft to open source projects related to Apache Spark.
Building data pipelines for modern data warehouse with Apache® Spark™ and .NET in Azure (Microsoft Build 2019)
- 5. 5 Data sources ETL Increasing data volumes 1 Non-relationaldata New data sources and types 2 Cloud-born data 3 DESIGNED FOR THE QUESTIONS YOU KNOW!
- 6. The data lakeapproach Ingest all data regardless of requirements Store all data in native format without schema definition Do analysis Schematize for scenario, run scale out analysis Interactive queries Batch queries Machine Learning Data warehouse Real-time analytics Devices
- 7. The modern datawarehouse extends the scope of the data warehouse to serve big data that’s prepared with techniques beyond relational ETL Modern data warehousing “We want to integrate all our data—including big data—with our data warehouse” Advanced analytics Advanced analytics is the process of applying machine learning and deep learning techniques to data for the purpose of creating predictive and prescriptive insights “We’re trying to predict when our customers churn” Real-time analytics “We’re trying to get insights from our devices in real-time”
- 8. INGEST STORE PREP& TRAIN MODEL & SERVE Azure Data Lake Storage Logs, files and media (unstructured) Azure SQL Data Warehouse Azure Data Factory Azure Analysis Services Azure Databricks (Python, Scala, Spark SQL, .NET for Apache Spark) Polybase Business/custom apps (Structured) Power BI Azure also supports other Big Data services like Azure HDInsight and Azure Data Lake to allow customers to tailor the above architecture to meet their unique needs. ORCHESTRATION & DATA FLOW ETL Azure Data Factory
- 10. Apache Sparkis an OSS fast analytics engine for big data and machine learning Improves efficiency through: General computation graphs beyond map/reduce In-memory computing primitives Allows developers to scale out their user code & write in their language of choice Rich APIs in Java, Scala, Python, R, SparkSQL etc. Batch processing, streaming and interactive shell Available on Azure via Azure Databricks Azure HDInsight IaaS/Kubernetes
- 11. A lot ofbig data-usable business logic (millions of lines of code) is written in .NET! Expensive and difficult to translate into Python/Scala/Java! Locked out from big data processing due to lack of .NET support in OSS big data solutions In a recently conducted .NET Developer survey (> 1000 developers), more than 70% expressed interest in Apache Spark! Would like to tap into OSS eco-system for: Code libraries, support, hiring
- 12. Goal: .NET forApache Spark is aimed at providing .NET developers a first-class experience when working with Apache Spark. Non-Goal: Converting existing Scala/Python/Java Spark developers.
- 13. • Interop layerfor .NET (Scala-side) • Potentially optimizing Python and R interop layers • Technical documentation, blogs and articles • End-to-end scenarios • Performance benchmarking (cluster) • Production workloads • Out of Box with Azure HDInsight, easy to use with Azure Databricks • C# (and F#) language extensions using .NET • Performance benchmarking (Interop) • Portability aspects (e.g., cross-platform .NET Standard) • Tooling (e.g., Apache Jupyter, Visual Studio, Visual Studio Code) Microsoft is committed…
- 14. Contributions to foundationalOSS projects: • Apache arrow: ARROW-4997, ARROW-5019, ARROW-4839, ARROW- 4502, ARROW-4737, ARROW-4543, ARROW-4435 • Pyrolite (pickling library): Improve pickling/unpickling performance, Add a Strong Name to Pyrolite .NET for Apache Spark was open sourced @Spark+AI Summit 2019 • Website: https://dot.net/spark • GitHub: https://github.com/dotnet/spark Spark project improvement proposals: • Interop support for Spark language extensions: SPARK-26257 • .NET bindings for Apache Spark: SPARK-27006
- 15. Spark DataFrames with SparkSQL workswith Spark v2.3.x/v2.4.[0/1] and includes ~300 SparkSQL functions .NET Spark UDFs Batch & streaming including Spark Structured Streaming and all Spark-supported data sources .NET Standard 2.0 works with .NET Framework v4.6.1+ and .NET Core v2.1+ and includes C#/F# support .NET Standard Machine Learning Including access to ML.NET Speed & productivity Performance optimized interop, as fast or faster than pySpark https://github.com/dotnet/spark/examples
- 16. var spark =SparkSession.Builder().GetOrCreate(); var dataframe = spark.Read().Json(“input.json”); dataframe.Filter(df["age"] > 21) .Select(concat(df[“age”], df[“name”]).Show(); var concat = Udf<int?, string, string>((age, name)=>name+age);
- 17. val europe =region.filter($"r_name" === "EUROPE") .join(nation, $"r_regionkey" === nation("n_regionkey")) .join(supplier, $"n_nationkey" === supplier("s_nationkey")) .join(partsupp, supplier("s_suppkey") === partsupp("ps_suppkey")) val brass = part.filter(part("p_size") === 15 && part("p_type").endsWith("BRASS")) .join(europe, europe("ps_partkey") === $"p_partkey") val minCost = brass.groupBy(brass("ps_partkey")) .agg(min("ps_supplycost").as("min")) brass.join(minCost, brass("ps_partkey") === minCost("ps_partkey")) .filter(brass("ps_supplycost") === minCost("min")) .select("s_acctbal", "s_name", "n_name", "p_partkey", "p_mfgr", "s_address", "s_phone", "s_comment") .sort($"s_acctbal".desc, $"n_name", $"s_name", $"p_partkey") .limit(100) .show() var europe = region.Filter(Col("r_name") == "EUROPE") .Join(nation, Col("r_regionkey") == nation["n_regionkey"]) .Join(supplier, Col("n_nationkey") == supplier["s_nationkey"]) .Join(partsupp, supplier["s_suppkey"] == partsupp["ps_suppkey"]); var brass = part.Filter(part["p_size"] == 15 & part["p_type"].EndsWith("BRASS")) .Join(europe, europe["ps_partkey"] == Col("p_partkey")); var minCost = brass.GroupBy(brass["ps_partkey"]) .Agg(Min("ps_supplycost").As("min")); brass.Join(minCost, brass["ps_partkey"] == minCost["ps_partkey"]) .Filter(brass["ps_supplycost"] == minCost["min"]) .Select("s_acctbal", "s_name", "n_name", "p_partkey", "p_mfgr", "s_address", "s_phone", "s_comment") .Sort(Col("s_acctbal").Desc(), Col("n_name"), Col("s_name"), Col("p_partkey")) .Limit(100) .Show(); Similar syntax – dangerously copy/paste friendly! $”col_name” vs. Col(“col_name”) Capitalization Scala C# C# vs Scala (e.g., == vs ===)
- 22. DataFrame SparkSQL .NET for Apache Spark .NET Program Did you define a.NET UDF? Regular execution path (no .NET runtime during execution) Interop between Spark and .NET No Yes Spark operation tree
- 23. Performance– warmcluster runs forPickling Serialization (Arrowwillbe tested inthe future) Takeaway1: Where UDF performance does not matter, .NET is on-par with Python Takeaway 2: Where UDF performance is critical, .NET is ~2x faster than Python!
- 24. Cross platform Cross Cloud WindowsUbuntu Azure & AWS Databricks macOS AWS EMR Spark Azure HDI Spark
- 25. • Spark .NETProject creation • Dependency packaging • Language service • Sample code Author • Reference management • Spark local run • Spark cluster run (e.g. HDInsight) Run • DebugFix Extension to VSCode Tap into VSCode for C# programming Automate Maven and Spark dependency for environment setup Facilitate first project success through project template and sample code Support Spark local run and cluster run Integrate with Azure for HDInsight clusters navigation Azure Databricks integration planned
- 26. More programming experiences in .NET (UDAF, UDT support,multi- language UDFs) Spark data connectors in .NET (e.g., Apache Kafka, Azure Blob Store, Azure Data Lake) Tooling experiences (e.g., Jupyter, VS Code, Visual Studio, others?) Idiomatic experiences for C# and F# (LINQ, Type Provider) Go to https://github.com/dotnet/spark and let us know what is important to you! Out-of-Box Experiences (Azure HDInsight, Azure Databricks, Cosmos DB Spark, SQL 2019 BDC, …)
- 29. CREATE EXTERNAL DATASOURCE MyADLSGen2 WITH (TYPE = Hadoop, LOCATION = ‘abfs://<filesys>@<account_name>.dfs.core.windows.net’, CREDENTIAL = <Database scoped credential>); CREATE EXTERNAL FILE FORMAT ParquetFile WITH ( FORMAT_TYPE = PARQUET, DATA_COMPRESSION = 'org.apache.hadoop.io.compress.GzipCodec’, FORMAT_OPTIONS (FIELD_TERMINATOR ='|', USE_TYPE_DEFAULT = TRUE)); CREATE EXTERNAL TABLE [dbo].[Customer_import] ( [SensorKey] int NOT NULL, int NOT NULL, [Speed] float NOT NULL) WITH (LOCATION=‘/Dimensions/customer', DATA_SOURCE = MyADLSGen2, FILE_FORMAT = ParquetFile) Once per store account (WASB, ADLS G1, ADLS G2) Once per file format, supports Parquet (snappy or Gzip), ORC, RC, CSV/TSV Folder path
- 30. CREATE TABLE [dbo].[Customer]WITH ( DISTRIBUTION = ROUND_ROBIN , CLUSTERED INDEX (customerid) ) AS SELECT * FROM [dbo].[Customer_import] INSERT INTO [dbo].[Customer] SELECT * FROM [dbo].[Customer_import] WHERE <predicate to determine new data>
- 32. Useful links: • http://github.com/dotnet/spark https://aka.ms/GoDotNetForSpark Website: •https://dot.net/spark Available out-of-box on Azure HDInsight Spark Running .NET for Spark anywhere— https://aka.ms/InstallDotNetForSparkYou & .NET
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