Spring Integration Tutorials

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Spring Integration is an open source framework for enterprise application integration. It is a lightweight framework that builds upon the core Spring framework. It is designed to enable the development of integration solutions typical of event-driven architectures and messaging-centric architectures.

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What is Garbage collection in Spark and its impact and resolution

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Garbage Collection Spark runs on the Java Virtual Machine (JVM). Because Spark can store large amounts of data in memory, it has a major reliance on Java’s memory management and garbage collection (GC). Therefore, garbage collection  (GC) can be a major issue that can affect many Spark applications. Common symptoms of excessive GC in Spark are: Slowness of application Executor heartbeat timeout GC overhead limit exceeded error Spark’s memory-centric approach and data-intensive applications make it a more common issue than other Java applications. Thankfully, it’s easy to diagnose if your Spark application is suffering from a GC problem. The Spark UI marks executors in red if they have spent too much time doing GC. Spark executors are spending a significant amount of CPU cycles performing garbage collection. This can be determined by looking at the “Executors” tab in the Spark application UI. Spark will mark an executor in  red  if the executor has spent more than 10% of the time in gar...
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How to change column name in Dataframe and selection of few columns in Dataframe using Pyspark with example

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Input details: #● File has json records #● Each record has fields: #○ user_id #○ card_num #○ merchant #○ category #○ amount #○ ts ### Below analysis to be done Sample data: +------+--------+---------+--------+----------+-------+|amount|card_num| category|merchant| ts|user_id|+------+--------+---------+--------+----------+-------+| 243| C_108| food| M_102|1579532902| U_104|| 699| C_106|cosmetics| M_103|1581759040| U_103|| 228| C_104| children| M_110|1584161986| U_103| Solution: from pyspark.sql.functions import col # this can be done without using window function creditCardData = spark.read.json(“card_transactions.json”) useridMaxSpendDF = df.groupby(‘user_id’).max(‘amount’) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“max(amount)”,”max_amount”) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“user_id”,”m_user_id”) cond = [creditCardData.user_id == useridMaxSpen...
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Window function in PySpark with Joins example using 2 Dataframes (inner join)

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Input details: #● File has json records #● Each record has fields: #○ user_id #○ card_num #○ merchant #○ category #○ amount #○ ts ### Below analysis to be done Sample data: +------+--------+---------+--------+----------+-------+|amount|card_num| category|merchant| ts|user_id|+------+--------+---------+--------+----------+-------+| 243| C_108| food| M_102|1579532902| U_104|| 699| C_106|cosmetics| M_103|1581759040| U_103|| 228| C_104| children| M_110|1584161986| U_103| Solution: from pyspark.sql.functions import col # this can be done without using window function creditCardData = spark.read.json(“card_transactions.json”) useridMaxSpendDF = df.groupby(‘user_id’).max(‘amount’) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“max(amount)”,”max_amount”) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“user_id”,”m_user_id”) cond = [creditCardData.user_id == useridMaxSpen...
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