Why is metadata consuming large amount of storage and how to optimize it?
I’m using PySpark with Apache Iceberg on an HDFS-based data lake, and I’m encountering significant storage issues. My application ingests real-time data every second. After approximately 2 hours, I get an error indicating that storage is full. Upon investigating the HDFS folder (which stores both data and metadata), I noticed that Iceberg’s metadata consumes a surprisingly large amount of storage compared to the actual data.
Why is metadata consuming large amount of storage and how to optimize it?
I’m using PySpark with Apache Iceberg on an HDFS-based data lake, and I’m encountering significant storage issues. My application ingests real-time data every second. After approximately 2 hours, I get an error indicating that storage is full. Upon investigating the HDFS folder (which stores both data and metadata), I noticed that Iceberg’s metadata consumes a surprisingly large amount of storage compared to the actual data.
Why is metadata consuming large amount of storage and how to optimize it?
I’m using PySpark with Apache Iceberg on an HDFS-based data lake, and I’m encountering significant storage issues. My application ingests real-time data every second. After approximately 2 hours, I get an error indicating that storage is full. Upon investigating the HDFS folder (which stores both data and metadata), I noticed that Iceberg’s metadata consumes a surprisingly large amount of storage compared to the actual data.
Why is metadata consuming large amount of storage and how to optimize it?
I’m using PySpark with Apache Iceberg on an HDFS-based data lake, and I’m encountering significant storage issues. My application ingests real-time data every second. After approximately 2 hours, I get an error indicating that storage is full. Upon investigating the HDFS folder (which stores both data and metadata), I noticed that Iceberg’s metadata consumes a surprisingly large amount of storage compared to the actual data.
Why is metadata consuming large amount of storage and how to optimize it?
I’m using PySpark with Apache Iceberg on an HDFS-based data lake, and I’m encountering significant storage issues. My application ingests real-time data every second. After approximately 2 hours, I get an error indicating that storage is full. Upon investigating the HDFS folder (which stores both data and metadata), I noticed that Iceberg’s metadata consumes a surprisingly large amount of storage compared to the actual data.