Storage structure and query performance optimization of ClickHouse primary key index

Table of Contents

1. Storage structure of primary key index

2. Query performance optimization methods

2.1. Use primary key index table

2.2. Column storage and data compression

2.3. Merge engine (MergeTree)

2.4. Data copy

in conclusion

Sample code: Use ClickHouse for e-commerce sales data analysis

Disadvantages of ClickHouse

similar database

Storage structure and query performance optimization of ClickHouse primary key index

ClickHouse is an open source distributed column storage database management system that is widely used in large-scale data analysis and data warehouse scenarios. As a column storage database, ClickHouse uses some efficient data structures to implement primary key indexes and improves query performance through a series of optimization techniques. This article will introduce the storage structure of ClickHouse primary key index and some query performance optimization methods.

1. Storage structure of primary key index

In ClickHouse, the primary key index is a data structure based on Bloom Filter. Bloom Filter is a probabilistic data structure used to determine whether an element belongs to a set. It trades extremely low space complexity for a certain query error. ClickHouse uses Bloom Filter to quickly determine whether a primary key exists in a partition. The specific storage structure is as follows:

Block: The basic unit of ClickHouse data storage is a block, and each block contains one or more columns of data. Each block has an independent Bloom Filter. Block sizes are typically tens to hundreds of MB.
Partition: Partition is a logical division unit of data in ClickHouse. It can be understood as a collection of data within a certain time period or under certain conditions. A partition can contain multiple blocks.
Primary Index Table: The primary key index table is a mapping relationship data structure that records the location information of each primary key and points to the corresponding partition and block. The data of the primary key index table is stored in memory. In order to improve query performance, it is designed to be highly compressed.

2. Query performance optimization method

2.1. Use the primary key to index the table

When performing a query, ClickHouse will first search for the corresponding primary key location information in the primary key index table based on the query conditions. By searching the primary key index table, you can quickly locate the partition and block where the data is located, avoiding the overhead of a full table scan.

2.2. Column storage and data compression

ClickHouse uses columnar storage to store the data of each column together, which can improve the data compression rate. ClickHouse supports a variety of data compression algorithms, such as LZ4, Zstd, etc. You can choose the appropriate compression algorithm based on the characteristics of the actual data.

2.3. Merge Engine (MergeTree)

ClickHouse’s merge engine is a commonly used data table storage engine. It can automatically merge small blocks into large blocks in the background, reducing storage space usage and improving query performance. The merge engine can trigger block merge operations based on user-defined time windows or data volumes.

2.4. Data Copy

ClickHouse supports redundant copy storage of data, which improves data availability and query performance by replicating data on multiple nodes. When data on one replica is unavailable, the system can obtain data from other replicas for query operations.

Conclusion

The storage structure and query performance optimization method of ClickHouse primary key index make it perform well in large-scale data analysis and data warehouse scenarios. By rationally utilizing the primary key index and combining it with other optimization methods, ClickHouse’s query performance can be improved and large amounts of data can be processed effectively. At the same time, understanding the storage structure and query performance optimization methods of ClickHouse primary key index will help us better apply and tune the ClickHouse database in practice.

Sample code: Use ClickHouse for e-commerce sales data analysis

pythonCopy codeimport clickhouse_driver
# Connect to ClickHouse database
conn = clickhouse_driver.connect(host='localhost', port=9000, user='username', password='password')
#Create sales data table
create_table_query = '''
    CREATE TABLE sales (
        date Date,
        product_id Int32,
        product_name String,
        price Float64,
        quantityInt32,
        total_amount Float64
    ) ENGINE = MergeTree()
    ORDER BY date
'''
conn.execute(create_table_query)
# Insert sales data
insert_data_query = '''
    INSERT INTO sales (
        date, product_id, product_name, price, quantity, total_amount
    ) VALUES (
        '2021-01-01', 1, 'Product A', 10.99, 20, 219.80
    ), (
        '2021-01-01', 2, 'Product B', 15.99, 15, 239.85
    ), (
        '2021-01-02', 1, 'Product A', 10.99, 10, 109.90
    )
'''
conn.execute(insert_data_query)
# Query the total sales per day
query_total_amount = '''
    SELECT date, sum(total_amount) as daily_total_amount
    FROM sales
    GROUP BY date
    ORDER BY date
'''
result = conn.execute(query_total_amount)
# Output query results
for row in result:
    date = row[0]
    total_amount = row[1]
    print(f"Date: {date}, Total Amount: {total_amount}")
#Close database connection
conn.disconnect()

This is a simple sample code that demonstrates how to use ClickHouse to store and analyze e-commerce sales data. First, a table named ??sales?? is created, which contains fields such as sales date, product ID, product name, price, quantity, and total amount. Then several sales records were added to the table by inserting data. Finally, use the query statement to calculate the total sales per day, sort by date, and print the results. This sample code is based on the Python language and uses the ??clickhouse_driver?? library to connect to the ClickHouse database and execute SQL statements. You can modify and extend the sample code according to actual scenarios to suit your specific needs. For example, you can add more fields and query conditions to perform more complex data analysis and query operations.

Disadvantages of ClickHouse

The learning curve is steep: ClickHouse’s syntax and query methods are different from traditional relational databases, requiring a certain amount of learning and adaptation costs. Especially for developers who have not been exposed to distributed databases or processed massive data, it may be difficult to get started.
Lack of real-time updates: ClickHouse is mainly used to process analytical queries of massive data, and has weak support for real-time data update requirements. The data writing operation takes a long time and is not suitable for real-time incremental update of data.
High hardware resource requirements: ClickHouse has relatively high requirements for computing resources and storage resources. When processing large-scale data, high-performance hardware and distributed clusters need to be configured to ensure query performance and throughput.
Lack of comprehensive transaction support: ClickHouse mainly focuses on fast aggregation queries, and its support for transactions is relatively weak. Although ClickHouse provides transaction-like functionality (such as support for rollback updates using the MergeTree engine), it is relatively difficult to operate complex transactions.

Similar database

Apache Hive: Hive is a data warehouse tool based on Hadoop, which can also be used for large-scale data analysis and query. Hive uses the SQL-like language HiveQL for querying and integrates seamlessly with other tools in the Hadoop ecosystem. Compared with ClickHouse, although Hive is slightly inferior in query performance, it is more suitable for the Hadoop-based ecosystem and better supports real-time data updates.
Apache Druid: Druid is a real-time analytics database focused on supporting fast, real-time OLAP queries. Druid uses distributed column storage and in-memory index technology to have low-latency query performance and can handle real-time data updates. Compared with ClickHouse, Druid is more suitable for scenarios that require real-time analysis, but it may be slightly inferior in handling massive data and complex queries.
Amazon Redshift: Redshift is a cloud data warehouse solution provided by Amazon AWS, which can also be used for analysis and query of massive data. Based on column storage and distributed computing, Redshift has high-performance query capabilities and scalability, and supports real-time data updates. Compared with ClickHouse, Redshift is more suitable for data analysis in cloud environments, but the price is relatively high. These similar databases have their own advantages and disadvantages, and choosing the appropriate database depends on specific needs and scenarios.