Presentation from Microsoft Data Science Summit 2016 Introduces U-SQL

4. The Data Lake approach
Ingest all data
regardless of
requirements
Store all data
in native format
without schema
definition
Do analysis
Using analytic
engines like Hadoop
Interactive queries
Batch queries
Machine Learning
Data warehouse
Real-time analytics
Devices

5. Introducing Azure Data Lake
Big Data Made Easy

6. WebHDFS
YARN
U-SQL
ADL Analytics ADL HDInsight
Store
HiveAnalytics
Storage
Azure Data Lake (Store, HDInsight, Analytics)

9. Some sample use cases
Digital Crime Unit – Analyze complex attack patterns
to understand BotNets and to predict and mitigate
future attacks by analyzing log records with
complex custom algorithms
Image Processing – Large-scale image feature
extraction and classification using custom code
Shopping Recommendation – Complex pattern
analysis and prediction over shopping records
using proprietary algorithms

10.  Declarativity does scaling and
parallelization for you
 Extensibility is bolted on and
not “native”
 hard to work with anything other than
structured data
 difficult to extend with custom code

11.  Extensibility through custom code
is “native”
 Declarativity is bolted on and
not “native”
 User often has to
care about scale and performance
 SQL is 2nd class within string
 Often no code reuse/
sharing across queries

12.  Declarativity and Extensibility are
equally native to the language!
Get benefits of both!
Makes it easy for you by unifying:
• Unstructured and structured data processing
• Declarative SQL and custom imperative Code
(C#)
• Local and remote Queries
• Increase productivity and agility from Day 1 and
at Day 100 for YOU!

13. The origins
of U-SQL
SCOPE – Microsoft’s internal
Big Data language
• SQL and C# integration model
• Optimization and Scaling model
• Runs 100’000s of jobs daily
Hive
• Complex data types (Maps, Arrays)
• Data format alignment for text files
T-SQL/ANSI SQL
• Many of the SQL capabilities (windowing functions, meta
data model etc.)

14. Benefits
• Avoid moving large amounts of data across the
network between stores
• Single view of data irrespective of physical location
• Minimize data proliferation issues caused by
maintaining multiple copies
• Single query language for all data
• Each data store maintains its own sovereignty
• Design choices based on the need
• Push SQL expressions to remote SQL sources
• Projections
• Filters
• Joins
U-SQL
Query
Query
Azure
Storage Blobs
Azure SQL
in VMs
Azure
SQL DB
Azure Data
Lake Analytics
Azure
SQL Data Warehouse
Azure
Data Lake Storage

15. https://github.com/Azure/usql/tree/master/Examples/TweetAnalysis

16. EXTRACT Expression
@s = EXTRACT a string, b int
FROM "filepath/file.csv"
USING Extractors.Csv(encoding: Encoding.Unicode);
• Built-in Extractors: Csv, Tsv, Text with lots of options
• Custom Extractors: e.g., JSON, XML, etc.
OUTPUT Expression
OUTPUT @s
TO "filepath/file.csv"
USING Outputters.Csv();
• Built-in Outputters: Csv, Tsv, Text
• Custom Outputters: e.g., JSON, XML, etc. (see http://usql.io)
Filepath URIs
• Relative URI to default ADL Storage account: "filepath/file.csv"
• Absolute URIs:
• ADLS: "adl://account.azuredatalakestore.net/filepath/file.csv"
• WASB: "wasb://container@account/filepath/file.csv"

17. U-SQL extensibility
Extend U-SQL with C#/.NET
Built-in operators,
function, aggregates
C# expressions (in SELECT expressions)
User-defined aggregates (UDAGGs)
User-defined functions (UDFs)
User-defined operators (UDOs)

18. https://github.com/Azure/usql/tree/master/Examples/TweetAnalysis

19. Managing
Assemblies
• CREATE ASSEMBLY db.assembly FROM @path;
• CREATE ASSEMBLY db.assembly FROM byte[];
• Can also include additional resource files
• REFERENCE ASSEMBLY db.assembly;
• Referencing .Net Framework Assemblies
• Always accessible system namespaces:
• U-SQL specific (e.g., for SQL.MAP)
• All provided by system.dll system.core.dll
system.data.dll, System.Runtime.Serialization.dll,
mscorelib.dll (e.g., System.Text,
System.Text.RegularExpressions, System.Linq)
• Add all other .Net Framework Assemblies with:
REFERENCE SYSTEM ASSEMBLY [System.XML];
• Enumerating Assemblies
• Powershell command
• U-SQL Studio Server Explorer
• DROP ASSEMBLY db.assembly;
 Create assemblies
 Reference assemblies
 Enumerate assemblies
 Drop assemblies
 VisualStudio makes registration easy!

20. 'USING' csharp_namespace
| Alias '=' csharp_namespace_or_class.
Examples:
DECLARE @ input string = "somejsonfile.json";
REFERENCE ASSEMBLY [Newtonsoft.Json];
REFERENCE ASSEMBLY [Microsoft.Analytics.Samples.Formats];
USING Microsoft.Analytics.Samples.Formats.Json;
@data0 =
EXTRACT IPAddresses string
FROM @input
USING new JsonExtractor("Devices[*]");
USING json =
[Microsoft.Analytics.Samples.Formats.Json.JsonExtractor];
@data1 =
EXTRACT IPAddresses string
FROM @input
USING new json("Devices[*]");

21. https://github.com/Azure/usql/tree/master/Examples/TweetAnalysis

22. Simple pattern language on filename and path
@pattern string =
"/input/{date:yyyy}/{date:MM}/{date:dd}/{*}.{suffix}";
• Binds two columns date and suffix
• Wildcards the filename
• Limits on number of files
(Current limit 800 and 3000 being increased in next refresh)
Virtual columns
EXTRACT
name string
, suffix string // virtual column
, date DateTime // virtual column
FROM @pattern
USING Extractors.Csv();
• Refer to virtual columns in query predicates to get partition
elimination
(otherwise you will get a warning)

23. https://github.com/Azure/usql/tree/master/Examples/TweetAnalysis

24. ADLA Account/Catalog
Database
Schema
[1,n]
[1,n]
[0,n]
tables views TVFs
C# Fns C# UDAgg
Clustered
Index
partitions
C#
Assemblies
C# Extractors
Data
Source
C# Reducers
C# Processors
C# Combiners
C# Outputters
Ext. tables
Abstract
objects
User
objects
Refers toContains Implemented
and named by
Procedures
Creden-
tials
MD
Name
C# Name
C# Applier
Table Types
Legend
Statistics
C# UDTs

25. • Naming
• Discovery
• Sharing
• Securing
U-SQL Catalog Naming
• Default Database and Schema context: master.dbo
• Quote identifiers with []: [my table]
• Stores data in ADL Storage /catalog folder
Discovery
• Visual Studio Server Explorer
• Azure Data Lake Analytics Portal
• SDKs and Azure Powershell commands
Sharing
• Within an Azure Data Lake Analytics account
Securing
• Secured with AAD principals at catalog level (inherited from ADL
Storage)
• At General Availability: Database level access control

26. CREATE TABLE T (col1 int
, col2 string
, col3 SQL.MAP<string,string>
, INDEX idx CLUSTERED (col1 ASC)
DISTRIBUTED BY HASH (driver_id)
);
• Structured Data
• Built-in Data types only (no UDTs)
• Clustered Index (needs to be specified): row-oriented
• Fine-grained distribution (needs to be specified):
• HASH, DIRECT HASH, RANGE, ROUND ROBIN
CREATE TABLE T (INDEX idx CLUSTERED …) AS SELECT …;
CREATE TABLE T (INDEX idx CLUSTERED …) AS EXTRACT…;
CREATE TABLE T (INDEX idx CLUSTERED …) AS myTVF(DEFAULT);
• Infer the schema from the query
• Still requires index and partitioning

27. https://github.com/Azure/usql/tree/master/Examples/TweetAnalysis

28. U-SQL
Joins
Join operators
• INNER JOIN
• LEFT or RIGHT or FULL OUTER JOIN
• CROSS JOIN
• SEMIJOIN
• equivalent to IN subquery
• ANTISEMIJOIN
• Equivalent to NOT IN subquery
Notes
• ON clause comparisons need to be of the simple form:
rowset.column == rowset.column
or AND conjunctions of the simple equality comparison
• If a comparand is not a column, wrap it into a column in a previous
SELECT
• If the comparison operation is not ==, put it into the WHERE clause
• turn the join into a CROSS JOIN if no equality comparison
Reason: Syntax calls out which joins are efficient

29. U-SQL
Analytics
Windowing Expression
Window_Function_Call 'OVER' '('
[ Over_Partition_By_Clause ]
[ Order_By_Clause ]
[ Row _Clause ]
')'.
Window_Function_Call :=
Aggregate_Function_Call
| Analytic_Function_Call
| Ranking_Function_Call.
Windowing Aggregate Functions
ANY_VALUE, AVG, COUNT, MAX, MIN, SUM, STDEV, STDEVP, VAR, VARP
Analytics Functions
CUME_DIST, FIRST_VALUE, LAST_VALUE, PERCENTILE_CONT,
PERCENTILE_DISC, PERCENT_RANK; soon: LEAD/LAG
Ranking Functions
DENSE_RANK, NTILE, RANK, ROW_NUMBER

30. “Top 5”s
Surprises for
SQL Users
• AS is not as
• C# keywords and SQL keywords overlap
• Costly to make case-insensitive -> Better build
capabilities than tinker with syntax
• = != ==
• Remember: C# expression language
• null IS NOT NULL
• C# nulls are two-valued
• PROCEDURES but no WHILE
• No UPDATE nor MERGE

32. U-SQL Language Philosophy
Declarative Query and Transformation Language:
• Uses SQL’s SELECT FROM WHERE with GROUP
BY/Aggregation, Joins, SQL Analytics functions
• Optimizable, Scalable
Expression-flow programming style:
• Easy to use functional lambda composition
• Composable, globally optimizable
Operates on Unstructured & Structured Data
• Schema on read over files
• Relational metadata objects (e.g. database, table)
Extensible from ground up:
• Type system is based on C#
• Expression language IS C#
• User-defined functions (U-SQL and C#)
• User-defined Aggregators (C#)
• User-defined Operators (UDO) (C#)
U-SQL provides the Parallelization and Scale-out
Framework for Usercode
• EXTRACTOR, OUTPUTTER, PROCESSOR, REDUCER,
COMBINER, APPLIER
Federated query across distributed data sources
REFERENCE MyDB.MyAssembly;
CREATE TABLE T( cid int, first_order DateTime
, last_order DateTime, order_count int
, order_amount float );
@o = EXTRACT oid int, cid int, odate DateTime, amount float
FROM "/input/orders.txt"
USING Extractors.Csv();
@c = EXTRACT cid int, name string, city string
FROM "/input/customers.txt"
USING Extractors.Csv();
@j = SELECT c.cid, MIN(o.odate) AS firstorder
, MAX(o.date) AS lastorder, COUNT(o.oid) AS ordercnt
, AGG<MyAgg.MySum>(c.amount) AS totalamount
FROM @c AS c LEFT OUTER JOIN @o AS o ON c.cid == o.cid
WHERE c.city.StartsWith("New")
&& MyNamespace.MyFunction(o.odate) > 10
GROUP BY c.cid;
OUTPUT @j TO "/output/result.txt"
USING new MyData.Write();
INSERT INTO T SELECT * FROM @j;

33. Unifies natively SQL’s declarativity and C#’s extensibility
Unifies querying structured and unstructured
Unifies local and remote queries
Increase productivity and agility from Day 1 forward for
YOU!
Sign up for an Azure Data Lake account and join the Public Preview
http://www.azure.com/datalake and give us your feedback via
http://aka.ms/adlfeedback or at http://aka.ms/u-sql-survey!

34. http://usql.io
http://blogs.msdn.microsoft.com/azuredatalake/
http://blogs.msdn.microsoft.com/mrys/
https://channel9.msdn.com/Search?term=U-SQL#ch9Search
http://aka.ms/usql_reference
https://azure.microsoft.com/en-
us/documentation/services/data-lake-analytics/
https://msdn.microsoft.com/en-us/magazine/mt614251
http://aka.ms/adlfeedback
https://social.msdn.microsoft.com/Forums/azure/en-
US/home?forum=AzureDataLake
http://stackoverflow.com/questions/tagged/u-sql