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Spark – Cannot perform Merge as multiple source rows matched…

June 18, 2021 1 comment

 

In SQL when you are syncing a table (target) from an another table (source) you need to make sure there are no duplicates or repeated datasets in either of the Source or Target tables, otherwise you get following error:

UnsupportedOperationException: Cannot perform Merge as multiple source rows matched and attempted to modify the same target row in the Delta table in possibly conflicting ways. By SQL semantics of Merge, when multiple source rows match on the same target row, the result may be ambiguous as it is unclear which source row should be used to update or delete the matching target row. You can preprocess the source table to eliminate the possibility of multiple matches. Please refer to https://docs.microsoft.com/azure/databricks/delta/delta-update#upsert-into-a-table-using-merge

The above error says that while doing MERGE operation on the Target table there shouldn’t be any duplicates in the Source table. This check is applied implicitly by the SQL engine to avoid unnecessary updates and avoid inconsistent data.

So, to avoid this issue make sure you have de-duplication logic before the MERGE operation.

 

Below is a small demo to reproduce this error.

Let’s create two sample tables (Source & Target) for our demo purpose:

val df1 = Seq((1, "Brock", 30), 
              (2, "John",  31), 
              (2, "Andy",  35), //duplicate ID = 2
              (3, "Jane",  25), 
              (4, "Maria", 30)).toDF("Id", "name", "age")

spark.sql("drop table if exists tblPerson")
df1.write.format("delta").saveAsTable("tblPerson")


val df2 = Seq((1, "Jane", 30),
              (2, "John", 31)).toDF("Id", "name", "age")

spark.sql("drop table if exists tblPersonTarget")
df2.write.format("delta").saveAsTable("tblPersonTarget")

 

Next we will try to MERGE the tables and running the query will result in an error:

val mergeQuery =
s"""MERGE INTO tblPersonTarget As tgt
Using tblPerson as src      
  ON src.Id = tgt.ID
WHEN MATCHED 
  THEN UPDATE 
  SET
    tgt.name = src.name,
    tgt.age = src.age
WHEN NOT MATCHED
  THEN INSERT (
    ID,
    name,
    age
  )
  VALUES (
    src.ID,
    src.name,
    src.age
  )"""

spark.sql(mergeQuery)

 

To remove duplicates you can simply try removing by using window functions or some logic as per your business requirement:

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._

val df2 = df1.withColumn("rn", row_number().over(window.partitionBy("Id").orderBy("name")))
val df3 = df2.filter("rn = 1")

display(df3)

Cosmos DB & PySpark – Retrieve all attributes from all Collections under all Databases

April 12, 2021 Leave a comment

 

In one of my [previous post] we saw how to retrieve all attributes from the items (JSON document) of all Collections under all Databases by using C# .net code.

Here in this post we will see how we can retrieve the same information in Azure Databricks environment by using Python language instead of C# .net code.

 

So first of all you need to make sure that you have the Azure Cosmos DB SQL API library installed in your Databricks cluster. [Link if not done]

Then use the below script which:

1. First connects to Cosmos DB by using the CosmosClient() method.
2. Then it gets list of all Databases by using list_databases() method
3. Then iterate thru all databases and get list of all Containers by using list_containers() method.
4. Now again iterating thru all Containers and querying the items using the query_items() method.
5. The “metadataInfo” dictionary object is storing all the Keys & Values present in the Container item.
6. Then the List object with name “metadataList” stores all the Database, Container & Item level details stored in “metadataInfo” dictionary.

6. Finally we used the “metadataList” object to create a DataFrame by using createDataFrame() method.

Get the Cosmos Uri & Primary Key from the Cosmos DB Overview tab and apply in the code below:

import azure.cosmos.cosmos_client as cosmos_client
import azure.cosmos.errors as errors
import azure.cosmos.exceptions as exceptions
import azure.cosmos.http_constants as http_constants
import json

cosmosUri = "https://YourCosmosDBName.documents.azure.com:443/"
pKey = "PrimaryKey=="

client = cosmos_client.CosmosClient(cosmosUri, {'masterKey': pKey})

cosmosDBsList = client.list_databases()

#Create a list to store the metadata
metadataList = []

#Iterate over all DBs
for eachCosmosDBsList in cosmosDBsList:
  #print("nDatabase Name: {}".format(eachCosmosDBsList['id']))
  dbClient = client.get_database_client(eachCosmosDBsList['id'])
  
  #Iterate over all Containers
  for containersList in dbClient.list_containers():
    #print("n- Container Name: {}".format(containersList['id']))
    conClient = dbClient.get_container_client(containersList['id'])
    
    #Query Container and read just TOP 1 row
    for queryItems in conClient.query_items("select top 1 * from c", 
                                            enable_cross_partition_query=True):
      for itemKey, itemValue in queryItems.items():
        #print(itemKey, " = ", itemValue)
        
        #Create a dictionary to store metedata info at attribute/field level
        metadataInfo = {}
        metadataInfo["Source"] = eachCosmosDBsList['id']
        metadataInfo["Entity"] = containersList['id']
        metadataInfo["Attribute"] = itemKey
        metadataInfo["Value"] = itemValue

        metadataList.append(metadataInfo)

#print(metadataList)

from pyspark.sql.types import *

mySchema = StructType([ StructField("Source", StringType(), True)
                       ,StructField("Entity", StringType(), True)
                       ,StructField("Attribute", StringType(), True)
                       ,StructField("Value", StringType(), True)])

df = spark.createDataFrame(metadataList, schema=mySchema)

df.createOrReplaceTempView("metadataDF")

display(df)

Categories: Cosmos DB, Python Tags: , ,

Using Python in Azure Databricks with Cosmos DB – DDL & DML operations by using “Azure-Cosmos” library for Python

April 9, 2021 1 comment

 

In one of my [previous post] we saw how to connect to Cosmos DB from Databricks by using the Apache Spark to Azure Cosmos DB connector. But that connector is limited to read and write data in Cosmos DB from Databricks compute using Scala language.

Here in this post we will see how can we do more in terms of managing the whole Cosmos DB databases, containers/collections and the items (JSON documents) from Databricks by using the Azure Cosmos DB SQL API SDK for Python.

 

Here we will perform some DDL & DML operations on Cosmos DB such as:

– Creating a new Database
– Creating a new Container
– Inserting new items
– Read items from Container
– Upserting/Updating items in Container
– Deleting items from Container
– Finally deleting the Container and Database

 

So first go to your Azure Databricks cluster, Libraries tab, click on Install New, on the popup select PyPI, and type “azure-cosmos” under Package text box, finally click the Install button. This will install the Azure Cosmos DB SQL API library and will show up in the Libraries tab.

Databricks Cosmos Python

 

Use the below sample code to import the required libraries and establish connection with Cosmos DB. You need to get the Cosmos Uri & Primary Key from the Cosmos DB Overview tab and apply in the code below:

import azure.cosmos.cosmos_client as cosmos_client
from azure.cosmos import CosmosClient, PartitionKey, exceptions

cosmosUri = 'https://YourCosmosDBName.documents.azure.com:443/'
pKey = 'MasterPrimaryKey'

client = cosmos_client.CosmosClient(cosmosUri, {'masterKey': pKey})


# 1. Create a new Database:
newDatabaseName = 'ManojDB'

newDatabase = client.create_database(newDatabaseName)
print('\n1. Database created with name: ', newDatabase.id)


# 2. Get Database properties
dbClient = client.get_database_client(newDatabaseName)

dbProperties = dbClient.read()
print('\n2. DB Properties: ', dbProperties)


# 3. Create a new Container:
newContainerName = 'ManojContainer'

newContainer = dbClient.create_container(id=newContainerName, 
                                         partition_key=PartitionKey(path="/id"))
print('\n3. Container created with name: ', newContainer.id)


# 4. Create items in the Container:
containerClient = dbClient.get_container_client(newContainerName)

item1 = {'id' : '101', 'empId': 101, 
         'empFirstName': 'Manoj', 'empLastName': 'Pandey'}
containerClient.create_item(item1)

item2 = {'id' : '102', 'empId': 102, 
         'empFirstName': 'Saurabh', 'empLastName': 'Sharma'}
containerClient.create_item(item2)

item3 = {'id' : '103', 'empId': 103, 
         'empFirstName': 'Hitesh', 'empLastName': 'Kumar'}
containerClient.create_item(item3)

print('\n4. Inserted 3 items in ', newContainer.id)


# 5. Read items from Container:
print('\n5. Get all 3 items from Container:')

for items in containerClient.query_items(
        query='SELECT * FROM c',
        enable_cross_partition_query = True):
    print(items)

So till here we’ve created a Database & a Container in Cosmos DB, and inserted few items/records in it, as shown below:

CosmosDB Test

 

Now we will do some more DML operations like UPSERT/UPDATE & DELETE items from the collections:

# 6. Update/Upsert a item in Container:

updateItem = {'id' : '103', 'empId': 103, 
              'empFirstName': 'Hitesh', 'empLastName': 'Chouhan'}

containerClient.upsert_item(updateItem)

print('\n6. Updated LastName of EmpId = 103:')

for items in containerClient.query_items(
        query='SELECT * FROM c WHERE c.empId = 103',
        enable_cross_partition_query = True):
    print(items)


# 7. Delete an item from Container:

print('\n7. Delete item/record with EmpId = 103:')

for items in containerClient.query_items(
        query='SELECT * FROM c WHERE c.empId = 103',
        enable_cross_partition_query = True):
    containerClient.delete_item(items, partition_key='103')
    
for items in containerClient.query_items(
        query='SELECT * FROM c',
        enable_cross_partition_query = True):
    print(items)

 

Finally we will clean up all the stuff by deleting the Container and Databases that we created initially:

# 8. Delete Container

dbClient.delete_container(newContainer)

print('\n8. Deleted Container ', newContainer)


# 9. Delete Database

client.delete_database(newDatabaseName)

print('\n9. Deleted Database ', newDatabaseName)


Python error: while converting Pandas Dataframe or Python List to Spark Dataframe (Can not merge type)

April 8, 2021 Leave a comment

 

Data typecasting errors are common when you are working with different DataFrames across different languages, like here in this case I got datatype mixing error between Pandas & Spark dataframe:

import pandas as pd
pd_df = pd.DataFrame([(101, 'abc'), 
                      ('def', 201), 
                      ('xyz', 'pqr')], 
                     columns=['col1', 'col2'])

df = spark.createDataFrame(pd_df)
display(df)
TypeError:
field col1: Can not merge type <class 'pyspark.sql.types.longtype'> and 
<class 'pyspark.sql.types.stringtype'>

 

While converting the Pandas DataFrame to Spark DataFrame its throwing error as Spark is not able to infer correct data type for the columns due to mix type of data in columns.

In this case you just need to explicitly tell Spark to use a correct datatype by creating a new schema and using it in createDataFrame() definition shown below:

import pandas as pd
pd_df = pd.DataFrame([(101, 'abc'), 
                      ('def', 201), 
                      ('xyz', 'pqr')], 
                     columns=['col1', 'col2'])

from pyspark.sql.types import *
df_schema = StructType([StructField("col1", StringType(), True)\
                       ,StructField("col2", StringType(), True)])

df = spark.createDataFrame(pd_df, schema=df_schema)
display(df)

Connect to Cosmos DB from Databricks and read data by using Apache Spark to Azure Cosmos DB connector

April 7, 2021 1 comment

 

In this post we will using Databricks compute environment to connect to Cosmos DB and read data by using Apache Spark to Azure Cosmos DB connector.

 

First go to your Azure Databricks cluster and import the Azure Cosmos DB connector library. Download the library JAR from either [Maven links] or the [Uber JAR] on your local PC drive and install the new library.

Databricks CosmosDB Library

 

Now open a new Notebook with language as scala and use the code provided below.

To get the Cosmos DB instance Uri and Key go to the Azure portal -> Cosmos DB instance, from Overview tab go to Keys tab and copy the “URI” & “PRIMARY READ-ONLY KEY” key values in code below.

import org.joda.time._  
import org.joda.time.format._  

import com.microsoft.azure.cosmosdb.spark.schema._  
import com.microsoft.azure.cosmosdb.spark.CosmosDBSpark  
import com.microsoft.azure.cosmosdb.spark.config.Config  

import org.apache.spark.sql.functions._

val readerConfig = Config(Map( 
  "Endpoint" -> "https://YourCosmosDBname.documents.azure.com:443/", 
  "Masterkey" -> "YourPrimaryKey==", 
  "Database" -> "DatabaseName", 
  "Collection" -> "CollectionName"
  "query_custom" -> "select * from c" //optional
))

val df = spark.sqlContext.read.cosmosDB(readerConfig)
display(df)