U-SQL Killer Scenarios: Custom Processing, Big Cognition, Image and JSON Processing at scale (SQL Saturday 635)
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The document outlines the capabilities of U-SQL in processing and analyzing large datasets, emphasizing its extensibility through user-defined operators (UDOs), integration with cognitive services, and support for custom processing languages like Python and R. It details the creation and deployment of UDOs, covers image and JSON processing, and introduces cognitive services for tasks such as sentiment analysis and object detection. The document also provides resources for further learning and best practices for efficient U-SQL usage.
![[SqlUserDefinedExtractor]
public class DriverExtractor : IExtractor
{
private byte[] _row_delim;
private string _col_delim;
private Encoding _encoding;
// Define a non-default constructor since I want to pass in my own parameters
public DriverExtractor( string row_delim = "rn", string col_delim = ",“
, Encoding encoding = null )
{
_encoding = encoding == null ? Encoding.UTF8 : encoding;
_row_delim = _encoding.GetBytes(row_delim);
_col_delim = col_delim;
} // DriverExtractor
// Converting text to target schema
private void OutputValueAtCol_I(string c, int i, IUpdatableRow outputrow)
{
var schema = outputrow.Schema;
if (schema[i].Type == typeof(int))
{
var tmp = Convert.ToInt32(c);
outputrow.Set(i, tmp);
}
...
} //SerializeCol
public override IEnumerable<IRow> Extract( IUnstructuredReader input
, IUpdatableRow outputrow)
{
foreach (var row in input.Split(_row_delim))
{
using(var s = new StreamReader(row, _encoding))
{
int i = 0;
foreach (var c in s.ReadToEnd().Split(new[] { _col_delim }, StringSplitOptions.None))
{
OutputValueAtCol_I(c, i++, outputrow);
} // foreach
} // using
yield return outputrow.AsReadOnly();
} // foreach
} // Extract
} // class DriverExtractor
UDO model
Marking UDOs
Parameterizing UDOs
UDO signature
UDO-specific processing
pattern
Rowsets and their schemas in
UDOs
Setting results
By position
By name](https://image.slidesharecdn.com/usqlkillerscenariosqlsaturday-170901003839/85/U-SQL-Killer-Scenarios-Custom-Processing-Big-Cognition-Image-and-JSON-Processing-at-scale-SQL-Saturday-635-9-320.jpg)
![Managing
Assemblies
Create assemblies
Reference assemblies
Enumerate assemblies
Drop assemblies
VisualStudio makes registration easy!
• 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 and Azure Portal
• DROP ASSEMBLY db.assembly;](https://image.slidesharecdn.com/usqlkillerscenariosqlsaturday-170901003839/85/U-SQL-Killer-Scenarios-Custom-Processing-Big-Cognition-Image-and-JSON-Processing-at-scale-SQL-Saturday-635-10-320.jpg)
![REFERENCE ASSEMBLY ImageCommon;
REFERENCE ASSEMBLY FaceSdk;
REFERENCE ASSEMBLY ImageEmotion;
REFERENCE ASSEMBLY ImageTagging;
REFERENCE ASSEMBLY ImageOcr;
@imgs =
EXTRACT FileName string, ImgData byte[]
FROM @"/images/{FileName}.jpg"
USING new Cognition.Vision.ImageExtractor();
// Extract the number of objects on each image and tag them
@objects =
PROCESS @imgs
PRODUCE FileName,
NumObjects int,
Tags string
READONLY FileName
USING new Cognition.Vision.ImageTagger();
OUTPUT @objects
TO "/objects.tsv"
USING Outputters.Tsv();
Imaging](https://image.slidesharecdn.com/usqlkillerscenariosqlsaturday-170901003839/85/U-SQL-Killer-Scenarios-Custom-Processing-Big-Cognition-Image-and-JSON-Processing-at-scale-SQL-Saturday-635-14-320.jpg)
![REFERENCE ASSEMBLY [TextCommon];
REFERENCE ASSEMBLY [TextSentiment];
REFERENCE ASSEMBLY [TextKeyPhrase];
@WarAndPeace =
EXTRACT No int,
Year string,
Book string, Chapter string,
Text string
FROM @"/usqlext/samples/cognition/war_and_peace.csv"
USING Extractors.Csv();
@sentiment =
PROCESS @WarAndPeace
PRODUCE No,
Year,
Book, Chapter,
Text,
Sentiment string,
Conf double
USING new Cognition.Text.SentimentAnalyzer(true);
OUTPUT @sentinment
TO "/sentiment.tsv"
USING Outputters.Tsv();
Text Analysis](https://image.slidesharecdn.com/usqlkillerscenariosqlsaturday-170901003839/85/U-SQL-Killer-Scenarios-Custom-Processing-Big-Cognition-Image-and-JSON-Processing-at-scale-SQL-Saturday-635-15-320.jpg)
![REFERENCE ASSEMBLY [ExtPython];
DECLARE @myScript = @"
def get_mentions(tweet):
return ';'.join( ( w[1:] for w in tweet.split() if w[0]=='@' ) )
def usqlml_main(df):
del df['time']
del df['author']
df['mentions'] = df.tweet.apply(get_mentions)
del df['tweet']
return df
";
@t =
SELECT * FROM
(VALUES
("D1","T1","A1","@foo Hello World @bar"),
("D2","T2","A2","@baz Hello World @beer")
) AS D( date, time, author, tweet );
@m =
REDUCE @t ON date
PRODUCE date string, mentions string
USING new Extension.Python.Reducer(pyScript:@myScript);
Use U-SQL to create a massively
distributed program.
Executing Python code across many
nodes.
Using standard libraries such as
numpy and pandas.
Documentation:
https://docs.microsoft.com/en-
us/azure/data-lake-analytics/data-
lake-analytics-u-sql-python-
extensions
Python
Extensions](https://image.slidesharecdn.com/usqlkillerscenariosqlsaturday-170901003839/85/U-SQL-Killer-Scenarios-Custom-Processing-Big-Cognition-Image-and-JSON-Processing-at-scale-SQL-Saturday-635-18-320.jpg)
![R running in U-
SQL
Generate a linear
model
SampleScript_LM_Iris.R
REFERENCE ASSEMBLY [ExtR];
DECLARE @IrisData string = @"/usqlext/samples/R/iris.csv";
DECLARE @OutputFileModelSummary string =
@"/my/R/Output/LMModelSummaryCoefficientsIrisFromRCommand.txt";
DECLARE @myRScript = @"
inputFromUSQL$Species = as.factor(inputFromUSQL$Species)
lm.fit=lm(unclass(Species)~.-Par, data=inputFromUSQL)
#do not return readonly columns and make sure that the column names are the
same in usql and r scripts,
outputToUSQL=data.frame(summary(lm.fit)$coefficients)
colnames(outputToUSQL) <- c(""Estimate"", ""StdError"", ""tValue"", ""Pr"")
outputToUSQL";
@InputData =
EXTRACT SepalLength double, SepalWidth double, PetalLength double,
PetalWidth double, Species string
FROM @IrisData
USING Extractors.Csv();
@ExtendedData = SELECT 0 AS Par, * FROM @InputData;
@ModelCoefficients = REDUCE @ExtendedData ON Par
PRODUCE Par, Estimate double, StdError double, tValue double, Pr double
READONLY Par
USING new Extension.R.Reducer(command:@myRScript, rReturnType:"dataframe");
OUTPUT @ModelCoefficients TO @OutputFileModelSummary USING Outputters.Tsv();](https://image.slidesharecdn.com/usqlkillerscenariosqlsaturday-170901003839/85/U-SQL-Killer-Scenarios-Custom-Processing-Big-Cognition-Image-and-JSON-Processing-at-scale-SQL-Saturday-635-20-320.jpg)
![JSON
Processing
@json =
EXTRACT personid int,
name string,
addresses string
FROM @input
USING new Json.JsonExtractor(“[*].person");
@person =
SELECT personid,
name,
Json.JsonFunctions.JsonTuple(addresses)["address"] AS address_array
FROM @json;
@addresses = SELECT personid, name, Json.JsonFunctions.JsonTuple(address) AS address
FROM @person
CROSS APPLY
EXPLODE (Json.JsonFunctions.JsonTuple(address_array).Values) AS A(address);
@result =
SELECT personid,
name,
address["addressid"]AS addressid,
address["street"]AS street,
address["postcode"]AS postcode,
address["city"]AS city
FROM @addresses;](https://image.slidesharecdn.com/usqlkillerscenariosqlsaturday-170901003839/85/U-SQL-Killer-Scenarios-Custom-Processing-Big-Cognition-Image-and-JSON-Processing-at-scale-SQL-Saturday-635-24-320.jpg)
U-SQL Killer Scenarios: Custom Processing, Big Cognition, Image and JSON Processing at scale (SQL Saturday 635)
- 1. U-SQL Killer scenarios: Custom Processing, Big Cognition, Image and JSON processing at Scale Michael Rys (@MikeDoesBigData) John Morcos Microsoft Corp
- 2. Agenda Introduction to U-SQL’s Extensibility U-SQL Cognitive Services More Custom Image processing Python in U-SQL R in U-SQL JSON processing
- 3. U-SQL extensibility Extend U-SQL with C#/.NET, Python, R etc. Built-in operators, function, aggregates C# expressions (in SELECT expressions) User-defined aggregates (UDAGGs) User-defined functions (UDFs) User-defined operators (UDOs)
- 4. What are UDOs? User-Defined Extractors User-Defined Outputters User-Defined Processors Take one row and produce one row Pass-through versus transforming User-Defined Appliers Take one row and produce 0 to n rows Used with OUTER/CROSS APPLY User-Defined Combiners Combines rowsets (like a user-defined join) User-Defined Reducers Take n rows and produce m rows (normally m<n) Scaled out with explicit U-SQL Syntax that takes a UDO instance (created as part of the execution): EXTRACT OUTPUT CROSS APPLY Custom Operator Extensions Scaled out by U-SQL PROCESS COMBINE REDUCE
- 5. Copyright Camera Make Camera Model Thumbnail Michael Canon 70D Michael Samsung S7 https://github.com/Azure/usql/tree/master/Examples/ImageApp
- 6. .Net API provided to build UDOs Any .Net language usable however only C# is first-class in tooling Use U-SQL specific .Net DLLs Deploying UDOs Compile DLL Upload DLL to ADLS register with U-SQL script VisualStudio provides tool support UDOs can Invoke managed code Invoke native code deployed with UDO assemblies Invoke other language runtimes (e.g., Python, R) be scaled out by U-SQL execution framework UDOs cannot Communicate between different UDO invocations Call Webservices/Reach outside the vertex boundary How to specify UDOs?
- 7. How to specify UDOs? Code behind
- 8. C# Class Project for U-SQLHow to specify UDOs?
- 9. [SqlUserDefinedExtractor] public class DriverExtractor : IExtractor { private byte[] _row_delim; private string _col_delim; private Encoding _encoding; // Define a non-default constructor since I want to pass in my own parameters public DriverExtractor( string row_delim = "rn", string col_delim = ",“ , Encoding encoding = null ) { _encoding = encoding == null ? Encoding.UTF8 : encoding; _row_delim = _encoding.GetBytes(row_delim); _col_delim = col_delim; } // DriverExtractor // Converting text to target schema private void OutputValueAtCol_I(string c, int i, IUpdatableRow outputrow) { var schema = outputrow.Schema; if (schema[i].Type == typeof(int)) { var tmp = Convert.ToInt32(c); outputrow.Set(i, tmp); } ... } //SerializeCol public override IEnumerable<IRow> Extract( IUnstructuredReader input , IUpdatableRow outputrow) { foreach (var row in input.Split(_row_delim)) { using(var s = new StreamReader(row, _encoding)) { int i = 0; foreach (var c in s.ReadToEnd().Split(new[] { _col_delim }, StringSplitOptions.None)) { OutputValueAtCol_I(c, i++, outputrow); } // foreach } // using yield return outputrow.AsReadOnly(); } // foreach } // Extract } // class DriverExtractor UDO model Marking UDOs Parameterizing UDOs UDO signature UDO-specific processing pattern Rowsets and their schemas in UDOs Setting results By position By name
- 10. Managing Assemblies Create assemblies Reference assemblies Enumerate assemblies Drop assemblies VisualStudio makes registration easy! • 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 and Azure Portal • DROP ASSEMBLY db.assembly;
- 11. DEPLOY RESOURCE Syntax: 'DEPLOY' 'RESOURCE' file_path_URI { ',' file_path_URI }. Example: DEPLOY RESOURCE "/config/configfile.xml", "package.zip"; Semantics: • Files have to be in ADLS or WASB • Files are deployed to vertex and are accessible from any custom code Limits: • Single resource file limit is 400MB • Overall limit for deployed resource files is 3GB
- 12. U-SQL Vertex Code C# C++ Algebra Additional non-dll files & Deployed resources managed dll native dll Compilation output (in job folder) Compilation and Optimization U-SQL Metadata Service Deployed to Vertices REFERENCE ASSEMBLY ADLS DEPLOY RESOURCE System files (built-in Runtimes, Core DLLs, OS)
- 14. REFERENCE ASSEMBLY ImageCommon; REFERENCE ASSEMBLY FaceSdk; REFERENCE ASSEMBLY ImageEmotion; REFERENCE ASSEMBLY ImageTagging; REFERENCE ASSEMBLY ImageOcr; @imgs = EXTRACT FileName string, ImgData byte[] FROM @"/images/{FileName}.jpg" USING new Cognition.Vision.ImageExtractor(); // Extract the number of objects on each image and tag them @objects = PROCESS @imgs PRODUCE FileName, NumObjects int, Tags string READONLY FileName USING new Cognition.Vision.ImageTagger(); OUTPUT @objects TO "/objects.tsv" USING Outputters.Tsv(); Imaging
- 15. REFERENCE ASSEMBLY [TextCommon]; REFERENCE ASSEMBLY [TextSentiment]; REFERENCE ASSEMBLY [TextKeyPhrase]; @WarAndPeace = EXTRACT No int, Year string, Book string, Chapter string, Text string FROM @"/usqlext/samples/cognition/war_and_peace.csv" USING Extractors.Csv(); @sentiment = PROCESS @WarAndPeace PRODUCE No, Year, Book, Chapter, Text, Sentiment string, Conf double USING new Cognition.Text.SentimentAnalyzer(true); OUTPUT @sentinment TO "/sentiment.tsv" USING Outputters.Tsv(); Text Analysis
- 16. U-SQL/Cognitive Example • Identify objects in images (tags) • Identify faces and emotions and images • Join datasets – find out which tags are associated with happiness REFERENCE ASSEMBLY ImageCommon; REFERENCE ASSEMBLY FaceSdk; REFERENCE ASSEMBLY ImageEmotion; REFERENCE ASSEMBLY ImageTagging; @objects = PROCESS MegaFaceView PRODUCE FileName, NumObjects int, Tags string READONLY FileName USING new Cognition.Vision.ImageTagger(); @tags = SELECT FileName, T.Tag FROM @objects CROSS APPLY EXPLODE(SqlArray.Create(Tags.Split(';'))) AS T(Tag) WHERE T.Tag.ToString().Contains("dog") OR T.Tag.ToString().Contains("cat"); @emotion_raw = PROCESS MegaFaceView PRODUCE FileName string, NumFaces int, Emotion string READONLY FileName USING new Cognition.Vision.EmotionAnalyzer(); @emotion = SELECT FileName, T.Emotion FROM @emotion_raw CROSS APPLY EXPLODE(SqlArray.Create(Emotion.Split(';'))) AS T(Emotion); @correlation = SELECT T.FileName, Emotion, Tag FROM @emotion AS E INNER JOIN @tags AS T ON E.FileName == T.FileName; Images Objects Emotions filter join aggregate
- 17. Python Processing Python Author Tweet MikeDoesBigData @AzureDataLake: Come and see the #SQLSaturday sessions on #USQL AzureDataLake What are your recommendations for #SQLSaturday? @MikeDoesBigData Author Mentions Topics MikeDoesBigData {@AzureDataLake} {#SQLSaturday, #USQL} AzureDataLake {@MikeDoesBigData} {#SQLSaturday}
- 18. REFERENCE ASSEMBLY [ExtPython]; DECLARE @myScript = @" def get_mentions(tweet): return ';'.join( ( w[1:] for w in tweet.split() if w[0]=='@' ) ) def usqlml_main(df): del df['time'] del df['author'] df['mentions'] = df.tweet.apply(get_mentions) del df['tweet'] return df "; @t = SELECT * FROM (VALUES ("D1","T1","A1","@foo Hello World @bar"), ("D2","T2","A2","@baz Hello World @beer") ) AS D( date, time, author, tweet ); @m = REDUCE @t ON date PRODUCE date string, mentions string USING new Extension.Python.Reducer(pyScript:@myScript); Use U-SQL to create a massively distributed program. Executing Python code across many nodes. Using standard libraries such as numpy and pandas. Documentation: https://docs.microsoft.com/en- us/azure/data-lake-analytics/data- lake-analytics-u-sql-python- extensions Python Extensions
- 19. R Processing
- 20. R running in U- SQL Generate a linear model SampleScript_LM_Iris.R REFERENCE ASSEMBLY [ExtR]; DECLARE @IrisData string = @"/usqlext/samples/R/iris.csv"; DECLARE @OutputFileModelSummary string = @"/my/R/Output/LMModelSummaryCoefficientsIrisFromRCommand.txt"; DECLARE @myRScript = @" inputFromUSQL$Species = as.factor(inputFromUSQL$Species) lm.fit=lm(unclass(Species)~.-Par, data=inputFromUSQL) #do not return readonly columns and make sure that the column names are the same in usql and r scripts, outputToUSQL=data.frame(summary(lm.fit)$coefficients) colnames(outputToUSQL) <- c(""Estimate"", ""StdError"", ""tValue"", ""Pr"") outputToUSQL"; @InputData = EXTRACT SepalLength double, SepalWidth double, PetalLength double, PetalWidth double, Species string FROM @IrisData USING Extractors.Csv(); @ExtendedData = SELECT 0 AS Par, * FROM @InputData; @ModelCoefficients = REDUCE @ExtendedData ON Par PRODUCE Par, Estimate double, StdError double, tValue double, Pr double READONLY Par USING new Extension.R.Reducer(command:@myRScript, rReturnType:"dataframe"); OUTPUT @ModelCoefficients TO @OutputFileModelSummary USING Outputters.Tsv();
- 21. R running in U- SQL Use a previously generated model REFERENCE ASSEMBLY master.ExtR; DEPLOY RESOURCE @"/usqlext/samples/R/my_model_LM_Iris.rda"; // Prediction Model DECLARE @IrisData string = @"/usqlext/samples/R/iris.csv"; DECLARE @OutputFilePredictions string = @"/Output/LMPredictionsIris.csv"; DECLARE @PartitionCount int = 10; // R script to run DECLARE @myRScript = @" load(""my_model_LM_Iris.rda"") outputToUSQL=data.frame(predict(lm.fit, inputFromUSQL, interval=""confidence""))"; @InputData = EXTRACT SepalLength double, SepalWidth double, PetalLength double, PetalWidth double, Species string FROM @IrisData USING Extractors.Csv(); //Randomly partition the data to apply the model in parallel @ExtendedData = SELECT Extension.R.RandomNumberGenerator.GetRandomNumber(@PartitionCount) AS Par, * FROM @InputData; // Predict Species @RScriptOutput = REDUCE @ExtendedData ON Par PRODUCE Par, fit double, lwr double, upr double READONLY Par USING new Extension.R.Reducer(command:@myRScript, rReturnType:"dataframe", stringsAsFactors:false); OUTPUT @RScriptOutput TO @OutputFilePredictions USING Outputters.Csv(outputHeader:true);
- 22. JSON Processing How do I extract data from JSON documents? https://github.com/Azure/usql/tree/master/Examples/DataFormats https://github.com/Azure/usql/tree/master/Examples/JSONExamples
- 23. Architecture of Sample Format Assembly Single JSON document per file: Use JsonExtractor Multiple JSON documents per file: Do not allow row delimiter (e.g., CR/LF) in JSON Use built-in Text Extractor to extract Use JsonTuple to schematize (with CROSS APPLY) Currently loads full JSON document into memory better to use JSONReader Processing if docs are large Microsoft.Analytics.Samples.Formats NewtonSoft.Json Microsoft.Hadoop.AvroSystem.Xml JSON Processing
- 24. JSON Processing @json = EXTRACT personid int, name string, addresses string FROM @input USING new Json.JsonExtractor(“[*].person"); @person = SELECT personid, name, Json.JsonFunctions.JsonTuple(addresses)["address"] AS address_array FROM @json; @addresses = SELECT personid, name, Json.JsonFunctions.JsonTuple(address) AS address FROM @person CROSS APPLY EXPLODE (Json.JsonFunctions.JsonTuple(address_array).Values) AS A(address); @result = SELECT personid, name, address["addressid"]AS addressid, address["street"]AS street, address["postcode"]AS postcode, address["city"]AS city FROM @addresses;
- 26. What are UDOs? Custom Operator Extensions written in .Net (C#) Scaled out by U-SQL
- 27. UDO Tips and Warnings Tips when Using UDOs: READONLY clause to allow pushing predicates through UDOs REQUIRED clause to allow column pruning through UDOs PRESORT on REDUCE if you need global order Hint Cardinality if it does choose the wrong plan Warnings and better alternatives: Use SELECT with UDFs instead of PROCESS Use User-defined Aggregators instead of REDUCE Learn to use Windowing Functions (OVER expression) Good use-cases for PROCESS/REDUCE/COMBINE: The logic needs to dynamically access the input and/or output schema. E.g., create a JSON doc for the data in the row where the columns are not known apriori. Your UDF based solution creates too much memory pressure and you can write your code more memory efficient in a UDO You need an ordered Aggregator or produce more than 1 row per group
- 28. Additional Resources Blogs and community page: http://usql.io (U-SQL Github) http://blogs.msdn.microsoft.com/azuredatalake/ http://blogs.msdn.microsoft.com/mrys/ https://channel9.msdn.com/Search?term=U-SQL#ch9Search Documentation, presentations and articles: http://aka.ms/usql_reference https://docs.microsoft.com/en-us/azure/data-lake-analytics/data-lake-analytics-u-sql- programmability-guide https://docs.microsoft.com/en-us/azure/data-lake-analytics/ https://msdn.microsoft.com/en-us/magazine/mt614251 https://msdn.microsoft.com/magazine/mt790200 http://www.slideshare.com/MichaelRys Getting Started with R in U-SQL https://docs.microsoft.com/en-us/azure/data-lake-analytics/data-lake-analytics-u-sql- python-extensions ADL forums and feedback https://social.msdn.microsoft.com/Forums/azure/en-US/home?forum=AzureDataLake http://stackoverflow.com/questions/tagged/u-sql http://aka.ms/adlfeedback
- 29. SQLSaturday Sponsors! Titanium & Global Partner Gold Silver Bronze Without the generosity of these sponsors, this event would not be possible! Please, stop by the vendor booths and thank them.
Editor's Notes
- #4: Extensions require .NET assemblies to be registered with a database