“Revolutionizing Data Management: The Power of Apache Hudi”
Introduction:
Managing large-scale data sets has become increasingly difficult for organizations in today’s digital age. As data volumes increase, traditional data management and processing methods become obsolete. This is where Apache Hudi enters the picture. Apache Hudi is a free and open-source data management framework that handles incremental data updates and manages large-scale data sets in real time.
What exactly is Apache Hudi?
The Apache Software Foundation created Apache Hudi, also known as Hadoop Upserts, Deletes, and Incrementals, as an open-source data management framework. Hudi is designed to handle incremental data updates and manage large-scale data sets in real-time in an efficient and scalable manner. Hudi uses Apache Spark’s distributed computing capabilities to process large data sets in parallel, reducing processing time and allowing organizations to scale their data processing needs as their business grows.
Key Features:
Hudi has several key features that make it an excellent solution for managing large-scale data sets. Hudi’s ability to perform upserts, deletes, and incremental updates is one of its most important features. This enables organizations to manage their data more efficiently and to make real-time updates.
Hudi’s ability to process data in near real-time is another key feature. This enables organisations to make data-driven decisions quickly and respond to data changes as they occur. The architecture of Hudi is based on the concept of Delta streams, which are append-only log files that contain data changes. When new data is added to a data set, Hudi generates a new Delta stream with the new data.
The Delta stream is then combined with the existing data set to create a new data snapshot.
Hudi is also scalable for large data sets. Hudi can process large data sets in parallel by leveraging Apache Spark’s distributed computing capabilities, reducing data processing time, and allowing organizations to scale their data processing needs as their business grows.
In addition to its key features, Hudi offers a number of additional features that help organizations manage their data more effectively. Hudi, for example, allows organizations to add or modify the schema of their data sets without having to stop or restart their data processing workflows. Hudi also supports data partitioning, which allows businesses to segment their data based on specific criteria such as date or geographic location.
Use Cases:
Hudi is used to manage large-scale data sets in a variety of industries. Hudi can be used in e-commerce to process real-time transactions and identify fraudulent transactions more quickly. Hudi can be used by financial institutions to process real-time financial transactions and detect anomalies. Hudi can be used by social media platforms to manage large amounts of user data and support real-time analytics.
Example of Hudi Upsert Process.
Suppose we have a dataset of employee information with the following schema:
| id | name | company | skill
+ — — + — — -+ — — — — + — — — -+
| 101|Vijay | TCS | Spark
| 102|Sanish | Axis | scala
| 103|Avinash | Paytm | java
| 104|Ashish | Jio | Nifi
| 105|Rahul | Accenture | ETL
Now let’s say that we want to perform an upsert operation on this dataset to update the employee record for Avinash, who recently moved from Paytm to Airtel. We can perform this operation using the following code:
import org.apache.spark.sql.SaveMode
import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.config.HoodieWriteConfig
import org.apache.spark.sql.functions.colval df = spark.read
.format("csv")
.option("header", "true")
.option("inferSchema", "true")
.load("employee_data.csv")
val hoodieOptions = Map[String, String](
HoodieWriteConfig.TABLE_NAME -> "employee_data",
DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY -> "id",
DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY -> "company",
DataSourceWriteOptions.KEYGENERATOR_CLASS_OPT_KEY -> "org.apache.hudi.keygen.SimpleKeyGenerator"
)
val updatedData = Seq(
(103, "Avinash", "Airtel", "java")
).toDF("id", "name", "company", "skill")
updatedData
.write
.format("org.apache.hudi")
.options(hoodieOptions)
.mode(SaveMode.Append)
.save("../../employee_data")
In this example, we first read the employee data from a CSV file and create our Hudi options, including the table name, record key field, partition path field, and key generator. We then define the updated data for Avinash and perform an upsert operation using the Hudi write API. Finally, we save the updated data back to the HDFS.
After running this code, the employee record for Avinash, would be updated in the dataset:
| id | name | company | skill
+ - - + - - -+ - - - - + - - - -+
| 101|Vijay | TCS | Spark
| 102|Sanish | Axis | scala
| 103|Avinash | Airtel | java
| 104|Ashish | Jio | Nifi
| 105|Rahul | Accenture | ETL
This is just a simple example of how Hudi can be used to perform an upsert operation on a dataset. In practice, Hudi can be used to manage much larger datasets and perform a wide range of operations, such as incremental processing and near real-time analytics.
Conclusion
In conclusion, Apache Hudi is a powerful data management framework that enables organizations to manage large-scale data sets in real-time in an efficient and scalable manner. Its ability to handle incremental updates, upserts, and deletes, and process data in near real-time makes it an ideal solution for organizations that require large amounts of data to be processed quickly and efficiently. Apache Hudi is a valuable tool for any organization looking to manage and process data more effectively, thanks to its robust features and architecture.
