Updating data in a Hive table

This can be achieved with out ORC file format and transaction=false, can be achieved only when the table is a partitioned table. This is a 2 step process:

1. Create data set with Updated entries using Union of non-updated records and New record in the partition.

select tbl2.street,tbl2.city,tbl2.zip,tbl2.state,tbl2.beds,tbl2.baths,tbl2.sq__ft,tbl2.sale_date,tbl2.price,tbl2.latitude,tbl2.longitude,tbl2.type from (select * from samp_tbl_part where type = "Multi-Family") tbl1 JOIN (select * from samp_tbl where type = "Multi-Family") tbl2 ON tbl1.zip=tbl2.zip         ///New Record
UNION ALL 
select tbl1.* from (select * from samp_tbl_part where type = "Multi-Family") tbl1 LEFT JOIN (select * from samp_tbl where type = "Multi-Family") tbl2 on tbl1.zip=tbl2.zip where tbl2.zip is NULL;       ////Non-updated records

2. Insert overwrite the partition.

Eg:
CREATE EXTERNAL TABLE `samp_tbl_part`(
  `street` string, 
  `city` string, 
  `zip` string, 
  `state` string, 
  `beds` string, 
  `baths` string, 
  `sq__ft` string, 
  `sale_date` string, 
  `price` string, 
  `latitude` string, 
  `longitude` string)
PARTITIONED BY ( 
  `type` string)
ROW FORMAT SERDE 
  'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
  'hdfs://quickstart.cloudera:8020/user/hive/sampledata/realestate_part';
  
220 OLD AIRPORT RD AUBURN 95603 CA 2 2 960 Mon May 19 00:00:00 EDT 2008 285000 38.939802 -121.054575 Multi-Family
398 LINDLEY DR SACRAMENTO 95815 CA 4 2 1744 Mon May 19 00:00:00 EDT 2008 416767 38.622359 -121.457582 Multi-Family
8198 STEVENSON AVE SACRAMENTO 95828 CA 6 4 2475 Fri May 16 00:00:00 EDT 2008 159900 38.465271 -121.40426 Multi-Family
1139 CLINTON RD SACRAMENTO 95825 CA 4 2 1776 Fri May 16 00:00:00 EDT 2008 221250 38.585291 -121.406824 Multi-Family
7351 GIGI PL SACRAMENTO 95828 CA 4 2 1859 Thu May 15 00:00:00 EDT 2008 170000 38.490606 -121.410173 Multi-Family

CREATE EXTERNAL TABLE `samp_tbl`(
  `street` string, 
  `city` string, 
  `zip` string, 
  `state` string, 
  `beds` string, 
  `baths` string, 
  `sq__ft` string, 
  `type` string, 
  `sale_date` string, 
  `price` string, 
  `latitude` string, 
  `longitude` string)
ROW FORMAT SERDE 
  'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' 
WITH SERDEPROPERTIES ( 
  'field.delim'=',', 
  'line.delim'='\n', 
  'serialization.format'=',') 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
  'hdfs://quickstart.cloudera:8020/user/hive/sampledata/realestate'

1139 CLINTON RD SACRAMENTO 95825 FL 4 2 1776 Multi-Family Fri May 16 00:00:00 EDT 2008 221250 38.585291 -121.406824
  
Complete Insert statement:
INSERT OVERWRITE TABLE samp_tbl_part partition (type) select tbl2.street,tbl2.city,tbl2.zip,tbl2.state,tbl2.beds,tbl2.baths,tbl2.sq__ft,tbl2.sale_date,tbl2.price,tbl2.latitude,tbl2.longitude,tbl2.type from (select * from samp_tbl_part where type = "Multi-Family") tbl1 JOIN (select * from samp_tbl where type = "Multi-Family") tbl2 ON tbl1.zip=tbl2.zip 
UNION ALL 
select tbl1.* from (select * from samp_tbl_part where type = "Multi-Family") tbl1 LEFT JOIN (select * from samp_tbl where type = "Multi-Family") tbl2 on tbl1.zip=tbl2.zip where tbl2.zip is NULL;

Configuring a Spark-submit Job

Configuring Spark-submit parameters

Before going further let's discuss on the below parameters which I have given for a Job.
spark.executor.cores=5 
spark.executor.instances=3
spark.executor.memory=20g
spark.driver.memory=5g 
spark.dynamicAllocation.enabled=true 
spark.dynamicAllocation.maxExecutors=10 

spark.executor.cores - specifies the number of cores for an executor. 5 is the optimum level of parallelism that can be obtained, More the number of executors can lead to bad HDFS I/O throughput.
More the cores, more the parallel tasks

spark.executor.instances - Specifies number of executors to run, so 3 Executors x 5 cores = 15 parallel tasks.

spark.executor.memory - The amount of memory each executor can get 20g(as per above configuration). Cores would be sharing this 20GB and as there are 5 cores each core/Task will get 20/5 = 4 GB.

Typically allocate 300 MB for processing 100,000 records.

spark.driver.memory - Usually this can be less as the driver manages the job and doesn't process the data. Driver also stores Local variables, needs larger space incase any Map/Array collection is allocated with large amounts of data. Usuall does Job allocation to executor nodes, DAG creation, writing Log, displaying in console etc

spark.dynamicAllocation.enabled - By default this is enabled and when there is a scope of using more resources than specified(when the cluster is free). The job will ramp up with the resources. Dominant resource calculator needs to be enabled to get the full power of Capacity. This will allocate executors and cores as per the ones we specified, if not enables default it will allocate 1 core per executor. Default is Memory based resource calculator.

spark.dynamicAllocation.maxExecutors - Can specify the maxExecutors in Dynamic allocation

When Dynamic allocation is enabled, the conf parameters look as below:

--conf spark.dynamicAllocation.enabled=true --conf spark.dynamicAllocation.minExecutors=1 --conf spark.dynamicAllocation.maxExecutors=3 --conf spark.executor.memory=20g --conf spark.driver.memory=5g

spark.yarn.executor.memoryOverhead - The amount of off-heap memory (in megabytes) to be allocated per executor. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc. This tends to grow with the executor size (typically 6-10%).

Off-heap refers to objects (serialised to byte array) that are managed by the operating system but stored outside the process heap in native memory (therefore, they are not processed by the garbage collector)

Coming to the actual story of assigning the parameters. 

Consider a case where data needs to be read from a partitioned table with each partition containing multiple small/medium files. In this case have Good executor memory, more executors and as usual 5 cores.

Similar cases as above but, not having multiple small/medium files at source. In this case executors can be less and can have good memory for each executor.

In case of incremental load where data pull is less, however needs to pull from multiple tables in parallel(Futures). In this case executors can be more, little less executor memory and as usual 5 cores.

Needs to consider amount of data being processed, the way joins are applied, stages in job, broadcast or not. Basically this goes as trail and error after analyzing the above factors and the best one can be chalked out in UAT environment.

Consider a 5 Node cluster with 12 cores in each node and 48 Gb RAM in each Node.A case when the complete cluster is allocated. Leave out 1 core and 1 GB for operational purposes.

FAT Executors: 

--num-executors 5

--num-executor-cores 11

--executor-memory 47g

Advantages:

-> Improved application performance in the cases when each task needs significant amount of data to be processed.

-> With fewer or Large executors, the chances of data being processed on the node where it is stored enhances data locality, there by reducing less network traffic.

Disadvantages: High possibility of resource under utilization.

Thin Executors: 

--num-executors 55

--num-executor-cores 1

--executor-memory 4g                ##Here per node 47Gb/11cores = 4GB. 

Advantages: 

-> Increases parallelism as there are more executors handling smaller tasks

-> Beneficial when the tasks are light weight.

-> Cases of Incremental load executed via Future execution context, streaming jobs etc

Disadvantages: Increased Network traffic, reduced data locality

Optimum Executors: 

--num-executor-cores 5      ##HDFS throughput deteriorate and leads to GC 

--num-executors 11

--num-executor-memory 20g

spark.driver.maxResultSize - Limit of total size of serialized results of all partitions for each Spark action (e.g. collect) in bytes. Should be at least 1M, or 0 for unlimited. Jobs will be aborted if the total size is above this limit. Having a high limit may cause out-of-memory errors in driver (depends on spark.driver.memory and memory overhead of objects in JVM). Setting a proper limit can protect the driver from out-of-memory errors.

Get Unravel Report and can tune accordingly.
There is a good monitoring tool called sparklens which can monitor a job and provide it's analysis in the first run. This is an open source.

Note: Incase, you want all you spark job load a particular dependency jars to drivers and executers then you can specify in those property. The --jars is if you want to add dependency jar to a spark job then we can explicitly pass to the spark-submit command as  --conf spark.driver.extraClassPath=<PATH>/phoenix5-spark-shaded.jar --conf spark.executor.extraClassPath=<PATH>/phoenix5-spark-shaded.jar 

Reference: https://youtu.be/mA96gUESVZc?si=EZSEl25hp9eH8PJ9

sparklens : https://docs.qubole.com/en/latest/user-guide/spark/sparklens.html

Creating Spark Scala SBT Project in Intellij

Below are the steps for creation Spark Scala SBT Project in Intellij:

1. Open Intellij via Run as Administrator and create a New project of type scala and sbt.
If this option is not available, open Intellij and go to settings -> pluging and type the plugin Scala and install it.
Also Install sbt plugin from the plugins window.

2. Select scala version which is compatible with spark, eg if spark version is 2.3 then select scala version as 2.11 and not 2.12 as spark 2.3 is compatible with scala 2.11. So, selected 2.11.8

Sample is available under https://drive.google.com/open?id=19YpCwLzuFZSqBReaceVOFS-BwlArOEpf

Debugging Spark Application

Remote Debugging

http://www.bigendiandata.com/2016-08-26-How-to-debug-remote-spark-jobs-with-IntelliJ/

1. Generate JAR file and copy it to a location in cluster.

2. Execute the command,
export SPARK_SUBMIT_OPTS=-agentlib:jdwp=transport=dt_socket,server=y,suspend=y,address=4000

3. In Intellij Run -> Edit Configurations -> Select Remote and Create a configuration with port number 4000 and the Host name of the machine in which JAR is copied.

4. submit the Spark application Eg: spark-submit --class com.practice.SayHello demo_2.11-0.1.jar

5. Click on Debug in Intellij for the configuration create in step3 and this would connect to the Spark Application.


To write data to Hive tables from Spark Dataframe below are the 2 steps:

1. In spark-submit add the entry of hive site file as --files /etc/spark/conf/hive-site.xml
2. Enable Hive support in spark session enableHiveSupport(). eg:
val spark = SparkSession.builder.appName("Demo App").enableHiveSupport().getOrCreate()

Sample Code:

   val date_add = udf((x: String) => {
      val sdf = new SimpleDateFormat("yyyy-MM-dd")
      val result = new Date(sdf.parse(x).getTime())
      sdf.format(result)
    } )

    val dfraw2 = dfraw.withColumn("ingestiondt",date_add($"current_date"))

dfraw2.write.format("parquet").mode(SaveMode.Append).partitionBy("ingestiondt").option("path", "s3://ed-raw/cdr/table1").saveAsTable("db1.table1")

XML Parsing

XML Parsing:

Source: https://medium.com/@tennysusanto/use-databricks-spark-xml-to-parse-nested-xml-d7d7cf797c28

Description:

This is a cool example and can be taken as a reference for most of the business scenarios.

In the below code, rowTag is mentioned as 'Transaction'. So in the contents between <<Transaction></<Transaction> would be read and formed as a structure with sub elements under it.

val df = sqlContext.read.format("com.databricks.spark.xml").option("rowTag", "Transaction").load("/user/tsusanto/POSLog-201409300635-21.xml")

df.printSchema  =>

root
 |-- BusinessDayDate: string (nullable = true)
 |-- ControlTransaction: struct (nullable = true)
 |    |-- OperatorSignOff: struct (nullable = true)
 |    |    |-- CloseBusinessDayDate: string (nullable = true)
 |    |    |-- CloseTransactionSequenceNumber: long (nullable = true)
 |    |    |-- EndDateTimestamp: string (nullable = true)
 |    |    |-- OpenBusinessDayDate: string (nullable = true)
 |    |    |-- OpenTransactionSequenceNumber: long (nullable = true)
 |    |    |-- StartDateTimestamp: string (nullable = true)
 |    |-- ReasonCode: string (nullable = true)
 |    |-- _Version: double (nullable = true)
 |-- CurrencyCode: string (nullable = true)
 |-- EndDateTime: string (nullable = true)
 |-- OperatorID: struct (nullable = true)
 |    |-- _OperatorName: string (nullable = true)
 |    |-- _VALUE: long (nullable = true)
 |-- RetailStoreID: long (nullable = true)
 |-- RetailTransaction: struct (nullable = true)
 |    |-- ItemCount: long (nullable = true)
 |    |-- LineItem: array (nullable = true)
 |    |    |-- element: struct (containsNull = true)
 |    |    |    |-- Sale: struct (nullable = true)
 |    |    |    |    |-- Description: string (nullable = true)
 |    |    |    |    |-- DiscountAmount: double (nullable = true)


 looking at the formed schema we can say that the element structure is formed as per XML format. For Sub elements like 'LineItem' the datatype is array of struct and it has elements like Sale(struct),Tax(struct),SequenceNumber(Long).

 Now, Flattening the contents in the LineItem. Explode is the function that can be used. withColumn will add a new column to the existing dataframe 'df'.
 val flattened = df.withColumn("LineItem", explode($"RetailTransaction.LineItem"))

 With this 'flattened' dataframe, the needed values can be extracted as like an SQL query. See $"LineItem.SequenceNumber",$"LineItem.Tax.TaxableAmount" in the below function as the way to extract the values to form a Table.

 val selectedData = flattened.select($"RetailStoreID",$"WorkstationID",$"OperatorID._OperatorName" as "OperatorName",$"OperatorID._VALUE" as "OperatorID",$"CurrencyCode",$"RetailTransaction.ReceiptDateTime",$"RetailTransaction.TransactionCount",$"LineItem.SequenceNumber",$"LineItem.Tax.TaxableAmount")

Explode Function: explode function creates a new row for each element in the given array or map column (in a DataFrame). In simple terms, Explode function is used to explode data in a structure.

After Explode, data in XML can be accessed via Tagname or _Tagname
<LineItem EntryMethod="Auto">
            <SequenceNumber>1</SequenceNumber>
            <Tax TaxID="1" TaxDescription="TaxID1">
               <TaxableAmount>5.04</TaxableAmount>
               <Amount>0.30</Amount>


Point to be noted is that for contents within tags the data can be accessed directly with the tagname.
Eg: for <TaxableAmount>5.04</TaxableAmount>
xmlflattened.select(col("LineItem.SequenceNumber"),col("LineItem.Tax.TaxableAmount")).show(5)

For, Tax TaxID="1" need to use _

xmlflattened.select(col("LineItem.SequenceNumber"),col("LineItem.Tax._TaxID")).show(5)

Extract XML from the RDBMS column where the data is compressed in GZIP Format

val gzipBinaryToString = udf((payload: Array[Byte]) => {

  val inputStream = new GZIPInputStream(new ByteArrayInputStream(payload))

  scala.io.Source.fromInputStream(inputStream).mkString

})

val data = xmldf.withColumn("xmlcolumn", gzipBinaryToString(unbase64($"coldata")))

Here, coldata is the column which contains XML in GZIP Format , xmldf is the dataframe, xmlcolumn is the New column in which we would like to extract the XML.

To read XML as a row value, 

from above data as a DF.

val xmlmodified = data.map(x => x.toString)

val reader = new XmlReader()

val xml_parsed = reader.withRowTag("Object").xmlrdd(spark.SqlContext,xmlmodified).select($"object")

StreamSets

Streamsets is a datapipeline tool and has multiple built in ready to use processors through which pipelines can be build. Few examples are below:

1. Incrementally ingest records from RDBMS to S3 location with lookups applied.
Select Origin as 'JDBC Query Consumer' -> Configuration 'JDBC' provide the JDBC URL.
jdbc:sqlserver://Databasehostname:1433;database=retaildb

 SQL Query:
SELECT A.*
,B1.TYPECODE as pctl_TypeCode,B1.NAME as pctl_Name,B1.DESCRIPTION as pctl_Description
FROM retail_contact A
LEFT OUTER JOIN pctl_typelist B1 ON (A.Subtype=B1.ID)
where A.UpdateTime > '${OFFSET}' ORDER BY A.UpdateTime

InitialOffset: 1970-01-01
Offset Column: UpdateTime

Destination: Amazon S3

Bucket: poc-bucket
Note: Don't provide s3:\\poc-bucket as this throws error. Just provide poc-bucket.

Common prefix: retaildb          //A directory name that can have the table data in this root directory.

Partition Prefix: Table1/ingestiondt=${YYYY()}-${MM()}-${DD()}     //This would ingest data as per date partition

Data Format: Delimited
Default Format: CSV
Header Line: With Header Line.

2. Incrementally ingest data from Multiple tables to independent directories as per table names Without Lookups.

Select Origin as 'JDBC Multitable Consumer' and provide 'JDBC connection String'
Schema: %
Table Pattern: retailtables_%
Offset Columns: UpdateTime


Select Destination as 'Amazon S3'
Bucket: poc-bucket
Common prefix: retaildb
Partition Prefix: ${record:attribute('jdbc.tables')}/ingestiondt=${YYYY()}-${MM()}-${DD()}   //This would create directories with table names and insert as per date Partition. 'jdbc.tables' is a variable through which table name is set.

If there is a need to pull data from a certain date(non-historical) then set,

Initial Offset: UpdateTime : ${time:dateTimeToMilliseconds(time:extractDateFromString('2018-10-01 00:00:00.000','yyyy-MM-dd HH:mm:ss.SSS'))}