Optimize IcebergCluster Performance In ClickHouse

by Alex Johnson 50 views

ClickHouse is a blazingly fast column-oriented database management system that's perfect for online analytical processing (OLAP). One of its powerful features is the ability to work with data stored in Apache Iceberg, an open table format for huge analytic datasets. When dealing with large datasets and complex queries using icebergHDFSCluster, you might encounter performance bottlenecks. This article explores how to identify and resolve these issues, ensuring your ClickHouse queries run as efficiently as possible.

Understanding the Performance Bottleneck

When using icebergHDFSCluster in ClickHouse, you might notice that the cluster read throughput is similar to that of a single node, which is not ideal for distributed processing. Let's consider a scenario where a query like the one below takes a significant amount of time to execute:

SELECT
    rtl_week_beg_dt AS week_begin_dt,
    age_for_rtl_week_id,
    coalesce(sum(snad_60d_cnt), 0) AS snad,
    coalesce(sum(elgb_trans_60d_cnt), 0) AS trans,
    coalesce(sum(bbe_60d_cnt), 0) AS bbe_trans
FROM icebergHDFSCluster('default', 'hdfs://hxxxx', 'auto')
WHERE (xxxx)
GROUP BY
    rtl_week_beg_dt,
    age_for_rtl_week_id
ORDER BY age_for_rtl_week_id;

If this query reads over 1000 data files and takes around 150 seconds to complete, with a throughput of approximately 0.5 GB/s, it indicates a potential bottleneck. For instance, the query might show statistics like:

43 rows in set. Elapsed: 197.053 sec. Processed 1.54 billion rows, 90.31 GB (7.84 million rows/s., 458.31 MB/s.)

To diagnose the performance issue, we can examine the partition pruning statistics, which might reveal that a large number of files are being scanned:

[ICEBERG] partition pruned files[on]: partition_pruned_files:3171, min-max index pruned files: 1782, not-pruned files: 1425

In this case, over a thousand files are being read, suggesting that the file processing overhead might be a contributing factor to the slow performance.

Profiling ClickHouse with Flame Graphs

Flame graphs are an invaluable tool for understanding where your application spends its time. To analyze ClickHouse query performance, you can generate flame graphs using the memory query profiler. Follow the steps outlined in the ClickHouse documentation to set up and generate a flame graph.

An example of a flame graph might highlight specific functions or processes that consume a significant portion of the execution time. In the scenario described, a flame graph revealed that about 20% of the time was spent in StorageObjectStorageStableTaskDistributor::getNexttask. This function is crucial for distributing tasks across the cluster, and high usage suggests inefficiencies in task distribution or management.

Identifying the Root Cause: ManifestFileEntry

Drilling down further, the flame graph might reveal that a significant portion of the time within StorageObjectStorageStableTaskDistributor::getNexttask is spent creating ManifestFileEntry objects. These objects are used to manage the metadata of the data files in Iceberg tables. If the creation and management of these entries become a bottleneck, it can severely impact query performance.

ManifestFileEntry objects store metadata about files in the Iceberg table, such as file paths, partition information, and statistics. If these objects are frequently created and copied, it can lead to significant overhead, especially when dealing with a large number of files. The flame graph below illustrates how much time is spent creating these entries.

ManifestFileEntry Creation

Solution: Optimizing ManifestFileEntry Management

To address the performance bottleneck related to ManifestFileEntry objects, one effective solution is to ensure that these objects are not copied unnecessarily. This can be achieved by making ManifestFileEntry non-copyable and using shared pointers to manage their lifecycle.

Making ManifestFileEntry Non-Copyable

To prevent unnecessary copying, modify the ManifestFileEntry struct to inherit from boost::noncopyable:

struct ManifestFileEntry : public boost::noncopyable

This ensures that the copy constructor and assignment operator are disabled for ManifestFileEntry, preventing accidental copies and reducing overhead.

Using Shared Pointers

Instead of passing ManifestFileEntry objects by value or raw pointers, use shared pointers (std::shared_ptr) to manage their lifetime. This ensures that the objects are properly deallocated when they are no longer needed, and it also avoids unnecessary copying.

using ManifestFileEntryPtr = std::shared_ptr<ManifestFileEntry>;

Modify all functions that handle ManifestFileEntry objects to accept ManifestFileEntryPtr instead of raw pointers or references. This ensures that the shared ownership of the objects is correctly managed.

Benefits of the Solution

By making ManifestFileEntry non-copyable and using shared pointers, you can significantly reduce the overhead associated with managing these objects. This leads to several benefits:

  • Reduced Memory Overhead: Shared pointers ensure that ManifestFileEntry objects are not copied, reducing memory usage and the strain on the garbage collector.
  • Improved Performance: By avoiding unnecessary copies, the time spent creating and managing ManifestFileEntry objects is reduced, leading to faster query execution times.
  • Better Scalability: The optimized approach scales better with larger datasets and more complex queries, as the overhead of managing ManifestFileEntry objects remains low.

Reproducing the Issue

To reproduce the performance issue, you need a ClickHouse setup that reads a large number of data files from an Iceberg table. This can be simulated by creating an Iceberg table with more than 1000 files and running a query that scans a significant portion of the data.

Steps to Reproduce

  1. Set up ClickHouse: Ensure you have a ClickHouse cluster configured and running.
  2. Create an Iceberg Table: Create an Iceberg table in HDFS with a large number of data files (e.g., 1000+ files). You can achieve this by partitioning your data and writing it in small chunks.
  3. Run a Query: Execute a query that reads data from the Iceberg table. The query should scan a significant portion of the data to trigger the performance issue.
  4. Monitor Performance: Observe the query execution time and the cluster read throughput. If the throughput is low (e.g., around 0.5 GB/s) and the query takes a long time to complete, it indicates the performance issue is present.
  5. Generate Flame Graphs: Use the ClickHouse memory query profiler to generate flame graphs while the query is running. This will help identify the functions that are consuming the most time.

Expected Performance Improvement

After applying the solution of making ManifestFileEntry non-copyable and using shared pointers, you should observe a significant improvement in query performance. The query execution time should decrease, and the cluster read throughput should increase.

The exact performance improvement will depend on the specific workload and the size of the dataset. However, in scenarios where the creation and management of ManifestFileEntry objects is a bottleneck, the improvement can be substantial.

Affected ClickHouse Versions

The performance issue related to ManifestFileEntry management affects all versions of ClickHouse that use the icebergHDFSCluster function. This is because the issue is inherent in the way ManifestFileEntry objects are handled within the task distribution process.

Additional Context and Considerations

When optimizing ClickHouse performance with Iceberg, consider the following additional factors:

  • Partitioning: Proper partitioning of your Iceberg table can significantly improve query performance. Choose partition keys that align with your query patterns to minimize the amount of data scanned.
  • Data Locality: Ensure that your data is stored in a way that minimizes network traffic. If your ClickHouse cluster and HDFS cluster are in different data centers, it can impact performance.
  • ClickHouse Configuration: Optimize ClickHouse configuration parameters, such as the number of threads and the amount of memory allocated for query processing, to maximize performance.
  • Compression: Use efficient compression codecs for your data files to reduce storage space and improve read performance.

Conclusion

Optimizing the performance of icebergHDFSCluster in ClickHouse involves identifying and addressing bottlenecks in task distribution and metadata management. By making ManifestFileEntry non-copyable and using shared pointers, you can significantly reduce the overhead associated with managing these objects, leading to improved query performance and scalability. Remember to profile your queries using flame graphs to identify specific performance issues and tailor your optimizations accordingly.

By implementing these strategies, you can ensure that ClickHouse efficiently processes your Iceberg data, providing fast and reliable query results. Always consider your specific workload and data characteristics when optimizing performance to achieve the best possible results.

For further reading on ClickHouse performance optimization, consider exploring resources like the Altinity Blog, which provides valuable insights and best practices for ClickHouse users.