High-Performance Parquet File Reading with ClickHouse Swarms

Follow the architecture and optimization strategies detailed below to achieve high-performance reads of Parquet files using swarms of ClickHouse® servers. This approach leverages ClickHouse’s distributed and columnar nature, combined with the efficiency of Parquet file handling.

1. Architecture Overview

Key Components

• Distributed ClickHouse Cluster:
  • A swarm of interconnected ClickHouse servers configured in a sharded and replicated architecture.
  • Ensures scalability, fault tolerance, and high availability.
• Parquet File Storage:
  • Parquet files stored on object storage systems like Amazon S3, Google Cloud Storage, or HDFS.
  • Parquet’s columnar format enables efficient I/O and compression.
• Data Ingestion & Query Orchestration:
  • Distributed table setup in ClickHouse to enable parallel reads.
  • External dictionaries or direct queries for metadata indexing.
• Clients:
  • BI Tools, dashboards, and applications querying the data.

2. Steps for High-Performance Reads

Step 1: Configure Distributed ClickHouse Cluster

• Sharding: Distribute data across multiple nodes using a consistent sharding key.
• Replication: Enable replicas to ensure fault tolerance and load balancing for read-heavy workloads.
• Topology Example:
  • N shards with M replicas each.
  • Shards handle data distribution, while replicas manage redundancy and load sharing.

CREATE TABLE parquet_data_distributed AS parquet_data
ENGINE = Distributed(cluster_name, database_name, parquet_data, sharding_key);

Step 2: Create External Storage Integration

ClickHouse can read Parquet files directly from external storage systems using table functions.

S3 Example:

SELECT * 
FROM s3('https://bucket-name.s3.amazonaws.com/file.parquet', 'AWS_ACCESS_KEY', 'AWS_SECRET_KEY', 'Parquet');

HDFS Example:

SELECT *
FROM hdfs('hdfs://namenode-host:8020/path/to/file.parquet', 'Parquet');

Local Parquet Files Example:

SELECT * FROM file('/path/to/file.parquet', 'Parquet');

Step 3: Optimize Parquet Reads

• Column Pruning:
  • ClickHouse reads only the required columns, leveraging Parquet’s columnar structure.
  • Use SELECT queries with specific columns.
• Partitioning:
  • Store Parquet files partitioned by common query dimensions (e.g., date, region).
  • This minimizes I/O during reads by allowing ClickHouse to skip irrelevant partitions.
• Predicate Pushdown:
  • ClickHouse pushes down filters directly to the Parquet reader, reducing the amount of data read.

SELECT column1, column2 
FROM s3('https://bucket-name.s3.amazonaws.com/file.parquet', 'AWS_ACCESS_KEY', 'AWS_SECRET_KEY', 'Parquet')
WHERE column1 = 'value';

Step 4: Predefine Metadata with External Dictionaries

If Parquet files are large and frequently accessed, external dictionaries can be used to predefine file metadata, reducing runtime lookup overhead.

CREATE DICTIONARY parquet_metadata_dict
(
    file_path String,
    file_size UInt64,
    last_modified DateTime
)
PRIMARY KEY file_path
SOURCE(CLICKHOUSE(
    host '127.0.0.1'
    port 9000
    user 'default'
    table 'parquet_metadata'
))
LAYOUT(FLAT())

Step 5: Materialized Views for Pre-Aggregation

For frequently queried Parquet data, pre-aggregate results into a ClickHouse table using materialized views.

CREATE MATERIALIZED VIEW parquet_aggregates
ENGINE = MergeTree()
PARTITION BY toYYYYMM(date_column)
ORDER BY (dimension1, dimension2)
POPULATE
AS
SELECT dimension1, dimension2, SUM(metric_column) AS total_metric
FROM s3('https://bucket-name.s3.amazonaws.com/file.parquet', 'AWS_ACCESS_KEY', 'AWS_SECRET_KEY', 'Parquet')
GROUP BY dimension1, dimension2;

Step 6: Distributed Queries

Use the Distributed Engine to query data across multiple nodes in parallel.

SELECT *
FROM parquet_data_distributed
WHERE filter_condition;

3. Best Practices

Performance Optimizations

• Enable Parallelism: Utilize ClickHouse’s ability to read Parquet files in parallel across shards and replicas.
• Compression: Leverage Parquet’s compression for reduced storage and faster reads.
• Merge Aggregations: Use pre-aggregated views or rollups to reduce computation during queries.

Capacity Planning

• Storage: Use NVMe SSDs for local caching and fast intermediate storage.
• Network: Ensure high-throughput network connections between ClickHouse nodes and storage (e.g., S3 or HDFS).
• Compute: Scale ClickHouse nodes based on concurrent query load and complexity.

Observability

• Monitor query performance using system.query_log and system.query_thread_log.
• Track disk I/O, memory usage, and network latency to identify bottlenecks.

4. Example Use Case

Scenario: Large-Scale Financial Data Analysis

• Data: Parquet files containing transaction logs, partitioned by transaction_date.
• Query: Aggregation of total transaction value by region and time.

Steps:

• Load Parquet data from S3:

SELECT region, SUM(transaction_value) 
FROM s3('https://bucket-name.s3.amazonaws.com/transactions.parquet', 'AWS_ACCESS_KEY', 'AWS_SECRET_KEY', 'Parquet')
GROUP BY region;

• Pre-aggregate into a materialized view:

CREATE MATERIALIZED VIEW transaction_summary
ENGINE = AggregatingMergeTree()
PARTITION BY toYYYYMM(transaction_date)
ORDER BY region
AS
SELECT region, SUM(transaction_value) AS total_value, COUNT() AS transaction_count
FROM s3('https://bucket-name.s3.amazonaws.com/transactions.parquet', 'AWS_ACCESS_KEY', 'AWS_SECRET_KEY', 'Parquet')
GROUP BY region;

• Distribute the data for parallel query execution:

CREATE TABLE transaction_summary_distributed AS transaction_summary
ENGINE = Distributed(cluster_name, database_name, transaction_summary, region);

• Query the distributed table:

SELECT region, total_value, transaction_count
FROM transaction_summary_distributed;

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