Optimize Ordering By Address & Balance In ClickHouse

Dec 2, 2025 by Alex Johnson 53 views

Optimizing Order By Address and Balance for Native Balances in ClickHouse

Handling large queries efficiently, especially when sorting by balance, can be a significant challenge. This article explores effective strategies for optimizing ORDER BY operations on the native_balances table in ClickHouse. We'll dive into maintaining performance while ensuring data accuracy, specifically focusing on scenarios where you need to sort by both address and balance, and keep only the most recent record for each address.

The Challenge: Ordering by Balance in Large Datasets

When dealing with substantial datasets, traditional database sorting methods can become slow and resource-intensive. The core challenge lies in how to efficiently sort by balance while also adhering to the constraint of having only one row per address, based on the latest block number. This requires a nuanced approach to table schema design and query optimization.

Understanding the Problem

Sorting by balance, especially in descending order (DESC), can be computationally expensive if the database has to scan the entire table. Adding the requirement to maintain only the latest record for each address further complicates the matter. We need to consider how ClickHouse's architecture and features can be leveraged to address these challenges.

Key Considerations

Data Volume: The sheer size of the native_balances table is a primary concern. Large tables mean more data to scan and sort.
Sorting Complexity: Sorting by balance requires the database to compare numerous values, which can be time-consuming.
Uniqueness Constraint: Ensuring only one row per address based on the latest block_num adds an extra layer of filtering and comparison.
Query Performance: The goal is to minimize query execution time, making the process efficient and responsive.

Strategies for Efficient Ordering in ClickHouse

ClickHouse offers several features and techniques that can be employed to optimize sorting and querying operations. Let's explore some strategies that can help in ordering by address and balance efficiently.

1. Table Schema Optimization

A well-designed table schema is the foundation of efficient queries. ClickHouse's MergeTree engine family provides various options for organizing data on disk, which can significantly impact query performance. Choosing the right primary key and sorting key is crucial.

Primary Key: The primary key in ClickHouse determines the order in which data is stored on disk. For our use case, including both address and block_num in the primary key is beneficial. This ensures that data is primarily sorted by address and then by block_num, which helps in quickly retrieving the latest record for each address.
```
CREATE TABLE native_balances (
    address String,
    block_num UInt32,
    balance Decimal128(18),
    -- other columns
    INDEX address_idx (address) TYPE minmax GRANULARITY 1
)
ENGINE = MergeTree()
PRIMARY KEY (address, block_num)
ORDER BY (address, block_num DESC);
```
Sorting Key: The ORDER BY clause in the CREATE TABLE statement specifies the sorting order within each data part. By including block_num DESC, we ensure that the most recent records are stored together, making it easier to retrieve the latest balance for each address.

2. Leveraging Materialized Views

Materialized views can precompute and store the results of a query, which can then be queried directly. This is particularly useful for aggregations and complex filtering operations. In our case, we can create a materialized view that keeps only the latest balance for each address.

Creating a Materialized View: A materialized view can be created to aggregate the balances by address, keeping only the row with the maximum block_num.

CREATE MATERIALIZED VIEW native_balances_latest
ENGINE = AggregatingMergeTree()
PARTITION BY intDiv(block_num, 100000)
ORDER BY (address)
AS
SELECT
    address,
    argMax(balance, block_num) AS balance,
    max(block_num) AS block_num
FROM native_balances
GROUP BY address;

Querying the Materialized View: Instead of querying the original table, you can query the materialized view, which is pre-aggregated and sorted.
```
SELECT address, balance
FROM native_balances_latest
ORDER BY balance DESC
LIMIT 100;
```

3. Using Projections

Projections in ClickHouse allow you to create alternative data layouts within the same table. This can be useful for optimizing specific queries without duplicating data. We can create a projection that sorts the data by balance, which can then be used for ORDER BY balance queries.

Creating a Projection: A projection can be created to sort the data by balance.

ALTER TABLE native_balances
ADD PROJECTION balance_proj
(
    SELECT
        address,
        block_num,
        balance
    ORDER BY balance DESC
);

Activating the Projection: ClickHouse will automatically use the projection when it's beneficial for a query. You can also explicitly specify the projection in your query.
```
SELECT address, balance
FROM native_balances
ORDER BY balance DESC
USING PROJECTION balance_proj
LIMIT 100;
```

4. Min/Max Count Projections for Filtering

ClickHouse's min/max indexes can be used to efficiently filter data based on ranges. While not directly related to sorting, these indexes can help reduce the amount of data that needs to be sorted, thereby improving performance. For example, you can filter out addresses with very low balances before sorting.

Creating a Min/Max Index: When creating the table, you can specify a min/max index on the balance column.

CREATE TABLE native_balances (
    address String,
    block_num UInt32,
    balance Decimal128(18),
    -- other columns
    INDEX balance_minmax (balance) TYPE minmax GRANULARITY 1
)
ENGINE = MergeTree()
PRIMARY KEY (address, block_num)
ORDER BY (address, block_num DESC);

Filtering by Balance: In your queries, use the WHERE clause to filter by balance, leveraging the min/max index.
```
SELECT address, balance
FROM native_balances
WHERE balance > 1000000
ORDER BY balance DESC
LIMIT 100;
```

5. Optimizing Data Insertion

The way data is inserted into ClickHouse can also affect query performance. Inserting data in batches and in the correct order can help ClickHouse optimize storage and indexing.

Batch Inserts: Insert data in large batches to reduce the overhead of merging data parts.
Ordered Inserts: If possible, insert data sorted by the primary key (address and block_num) to minimize data part merging.

6. Query Optimization Techniques

Beyond schema design, query optimization techniques can significantly improve performance.

Limiting Results: Use the LIMIT clause to restrict the number of results returned, especially when sorting large datasets.
Filtering: Apply filters using the WHERE clause to reduce the amount of data processed.
Avoiding SELECT *: Select only the necessary columns to reduce I/O overhead.

Practical Examples and Implementation

Let's walk through a practical example of how these techniques can be implemented to optimize ordering by address and balance in ClickHouse.

Scenario

We have a native_balances table with billions of rows, and we need to efficiently retrieve the top 100 addresses with the highest balances, ensuring we only consider the latest balance for each address.

Implementation Steps

Create the Table: Design the table with an appropriate primary key and sorting key.

CREATE TABLE native_balances (
    address String,
    block_num UInt32,
    balance Decimal128(18),
    -- other columns
)
ENGINE = MergeTree()
PRIMARY KEY (address, block_num)
ORDER BY (address, block_num DESC);

Create a Materialized View: Create a materialized view to keep only the latest balance for each address.

CREATE MATERIALIZED VIEW native_balances_latest
ENGINE = AggregatingMergeTree()
PARTITION BY intDiv(block_num, 100000)
ORDER BY (address)
AS
SELECT
    address,
    argMax(balance, block_num) AS balance,
    max(block_num) AS block_num
FROM native_balances
GROUP BY address;

Create a Projection: Add a projection to sort the data by balance.

ALTER TABLE native_balances_latest
ADD PROJECTION balance_proj
(
    SELECT
        address,
        balance
    ORDER BY balance DESC
);

Query the Materialized View: Query the materialized view to retrieve the top 100 addresses with the highest balances.
```
SELECT address, balance
FROM native_balances_latest
ORDER BY balance DESC
LIMIT 100;
```

Benefits

Improved Query Performance: The materialized view precomputes the latest balances, and the projection allows for efficient sorting by balance.
Reduced Resource Consumption: By querying the materialized view, we avoid scanning the entire native_balances table.
Simplified Queries: The query becomes simpler and easier to understand.

Conclusion

Optimizing ORDER BY operations in ClickHouse, especially for large datasets like native_balances, requires a combination of smart schema design, strategic use of materialized views and projections, and efficient query techniques. By carefully considering these factors, you can ensure high performance and accurate results. Remember, the key is to leverage ClickHouse's powerful features to preprocess and organize your data in a way that aligns with your query patterns.

By implementing these strategies, you can handle large queries efficiently while ensuring data accuracy and maintaining performance. ClickHouse's flexibility and powerful features make it well-suited for handling complex sorting and filtering requirements. For further reading on ClickHouse optimization techniques, visit the official ClickHouse documentation on https://clickhouse.com/docs/en/. 🛡️