Troubleshooting SQL statments with weak selectivity in ClickHouse
Tags: clickhouse, dataanalytics
Published: 2024-01-20 17:56:46

Troubleshooting performance issues of SQL statements with weak selectivity in ClickHouse involves several steps and techniques. Weak selectivity means the query conditions (usually in the `WHERE` clause) are not effective in filtering out a large number of rows, leading to inefficient processing and slower query performance. Here's a detailed guide with examples and steps to address this issue:

### 1. Identify Problematic Queries

First, you need to identify the queries that have performance issues. This can be done by monitoring slow queries. ClickHouse provides system logs and tables like `system.query_log` where you can find long-running queries.

**Example**:
```sql
SELECT query, elapsed
FROM system.query_log
WHERE type = 'QueryFinish'
  AND elapsed > X -- X is the threshold in seconds for query duration
ORDER BY elapsed DESC;
```

### 2. Analyze the Query Execution Plan

Once you've identified a slow query, use the `EXPLAIN` statement to understand how ClickHouse is processing the query.

**Example**:
```sql
EXPLAIN SYNTAX SELECT * FROM my_table WHERE weak_selectivity_condition;
```

Look for signs like full table scans or a large number of rows being read, which are indicators of weak selectivity.

### 3. Refine the WHERE Clause

Improve the selectivity of the `WHERE` clause:

- Narrow down the criteria.
- Use more specific predicates.
- Avoid using non-sargable expressions (those that cannot be benefited from indexes).

**Before Optimization**:
```sql
SELECT * FROM my_table WHERE toYear(date_column) = 2021;
```

**After Optimization**:
```sql
SELECT * FROM my_table WHERE date_column BETWEEN '2021-01-01' AND '2021-12-31';
```

### 4. Optimize Data Schema

- **Indexing**: Ensure that columns used in `WHERE` clauses are indexed efficiently. In ClickHouse, this is more about how data is partitioned and ordered.
- **Partitioning**: Use partitioning to divide the table into smaller chunks based on common query patterns.

**Example**:
```sql
CREATE TABLE my_table (
    ...
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(date_column)
ORDER BY (date_column, ...);
```

### 5. Use Sampling

If approximate results are acceptable, use ClickHouse’s sampling feature.

**Example**:
```sql
SELECT * FROM my_table SAMPLE 0.1 WHERE weak_selectivity_condition;
```

### 6. Rewrite or Split Queries

Sometimes, rewriting a query or breaking it into multiple smaller queries can improve performance.

**Before**:
```sql
SELECT * FROM large_table WHERE complex_condition;
```

**After**:
- Break into smaller, more specific queries.
- Use intermediate temporary tables if necessary.

### 7. Use Materialized Views

For frequently run queries with weak selectivity, consider using materialized views to pre-aggregate data.

**Example**:
```sql
CREATE MATERIALIZED VIEW my_view AS
SELECT agg_func(column), ... FROM my_table GROUP BY group_column;
```

### 8. Monitor and Adjust

After making changes, continuously monitor the query performance. You might need to adjust your strategies based on real-world performance data.

### 9. Query Logs and System Tables

Regularly review query logs and system tables like `system.metrics`, `system.asynchronous_metrics`, and `system.parts` to understand the overall health and performance of your ClickHouse instance.

By following these steps and continually monitoring performance, you can significantly improve the efficiency of SQL statements with weak selectivity in ClickHouse. Remember, each optimization should be tested in your specific environment to ensure it provides the desired performance improvement.

More blogs to read on ClickHouse Performance Troubleshooting: 

* https://chistadata.com/profiling-clickhouse-for-performance-troubleshooting/
* https://chistadata.com/demystifying-estimated-i-o-and-cpu-costs-in-clickhouse-for-effective-performance-troubleshooting/
* https://chistadata.com/troubleshooting-and-optimizing-high-cpu-usage-in-clickhouse-best-practices/