"GetPathStatus" - about 2.5 Billion requests a month "ListFileSystemDir" - about 0.8 Billion requests a month

Question

We are running a datalake using ADSL v2. Its just a BLOB store, and we run our Hive on top of it.

However despite having just around 50K queries a month... we're getting

2.5 Billion "GetPathStatus"
0.8 Billion "ListFileSystemDir"

per month.

that seems extremely over the top?

Answer 1

The high number of "GetPathStatus" and "ListFileSystemDir" operations in your data lake, despite a relatively low number of queries, does seem unusual. These operations are associated with file system metadata requests in Hadoop Distributed File System (HDFS) and can impact performance if they occur excessively.

Here are some steps you can take to investigate and potentially optimize this issue:

Review Query Patterns: Analyze your Hive queries and the way data is accessed. High numbers of metadata operations can occur if queries frequently scan large directories or if there are complex queries with many small files. Consider optimizing your data layout and query patterns.

Partitioning and Bucketing: Ensure that your data is properly partitioned and bucketed in Hive. This can significantly reduce the number of metadata operations required for queries.

Caching: Implement caching mechanisms like HDFS caching or query result caching to reduce the need for frequent metadata operations. Caching can help if certain data is accessed repeatedly.

Compression: Compressing small files can reduce the number of file metadata operations, as metadata is stored at the level of individual files. Combining small files into larger ones before loading them into the data lake can be helpful.

Optimize Storage Format: Consider using columnar storage formats like ORC or Parquet, which can improve query performance and reduce metadata operations by skipping irrelevant data.

Monitoring and Logging: Continuously monitor your HDFS and Hive clusters for unusual activity and enable detailed logging to identify the source of these excessive operations.

Upgrade and Configuration: Ensure that you're using the latest versions of Hadoop, Hive, and related components, as newer versions may have optimizations. Review and fine-tune HDFS and Hive configurations for your workload.

External Factors: Consider if there are external factors like backup processes, data replication, or third-party tools interacting with your data lake that might be causing excessive metadata operations.

HDFS Federation: If your data lake is very large, consider implementing HDFS federation, which allows you to divide the namespace into multiple sub-namespaces, potentially reducing the metadata operations in any one namespace.

Consult with Experts: If the issue persists and you can't identify the root cause, consider consulting with experts in Hadoop and Hive optimization to conduct a thorough review of your setup.

By taking these steps, you should be able to reduce the number of "GetPathStatus" and "ListFileSystemDir" operations, leading to improved performance and resource utilization in your data lake.

Answer 2

Hello @Hayder Aziz

The high number of "GetPathStatus" and "ListFileSystemDir" operations in your data lake could be due to a variety of reasons. Here are some additional steps you can take to investigate and potentially optimize this issue:

Check for excessive metadata operations: Use tools like Hadoop Metrics2 or Ambari Metrics to monitor the number of metadata operations in your data lake. If you see a high number of "GetPathStatus" and "ListFileSystemDir" operations, you may need to optimize your data layout and query patterns.

Optimize data layout: Ensure that your data is properly organized and partitioned in your data lake. This can help reduce the number of metadata operations required for queries.

Optimize query patterns: Review your Hive queries and optimize them to reduce the number of metadata operations. For example, you can use the "LIMIT" clause to limit the number of rows returned by a query, or use the "WHERE" clause to filter data before it is scanned.

Use columnar storage formats: Consider using columnar storage formats like ORC or Parquet, which can improve query performance and reduce metadata operations by skipping irrelevant data.

Upgrade and configure Hadoop: Ensure that you're using the latest versions of Hadoop and related components, as newer versions may have optimizations. Review and fine-tune Hadoop configurations for your workload.

Check for external factors: Consider if there are external factors like backup processes, data replication, or third-party tools interacting with your data lake that might be causing excessive metadata operations.

By taking these steps, you should be able to reduce the number of "GetPathStatus" and "ListFileSystemDir" operations, leading to improved performance and resource utilization in your data lake.