GreptimeDB Query Plan Ignoring Partitions: A Troubleshooting Guide

Dec 2, 2025 by Alex Johnson 67 views

Understanding Why GreptimeDB Query Plan Ignores Table Partitions

When working with time-series databases like GreptimeDB, table partitioning is a crucial technique for optimizing query performance. Partitioning allows you to divide your data into smaller, more manageable chunks based on certain criteria, such as time ranges or value ranges. This way, queries only need to scan the relevant partitions, significantly reducing the amount of data processed and improving query speed. However, there are situations when the query plan might not honor these partitions, leading to full table scans and slower query execution times.

The Issue: Query Plan Scanning All Partitions

One common issue arises when querying data using a specific column, like trace_id in the provided example, where the query plan ends up scanning all partitions instead of just the ones containing the relevant data. This can happen even if the table is partitioned on the trace_id column, as demonstrated in the user's scenario. The EXPLAIN output clearly shows a MergeScan operation across all partitions, indicating that the partition pruning is not working as expected. Let's delve deeper into the potential causes and solutions for this problem.

Analyzing the Table Schema and Partitioning Scheme

To effectively troubleshoot this issue, let's examine the provided table schema and partitioning scheme for opentelemetry_traces:

CREATE TABLE IF NOT EXISTS `opentelemetry_traces` (
 `timestamp` TIMESTAMP(9) NOT NULL,
 `timestamp_end` TIMESTAMP(9) NULL,
 `duration_nano` BIGINT UNSIGNED NULL,
 `parent_span_id` STRING NULL SKIPPING INDEX WITH(granularity = '10240', type = 'BLOOM'),
 `trace_id` STRING NULL SKIPPING INDEX WITH(granularity = '10240', type = 'BLOOM'),
 `span_id` STRING NULL,
 `span_kind` STRING NULL,
 `span_name` STRING NULL,
 `span_status_code` STRING NULL,
 `span_status_message` STRING NULL,
 `trace_state` STRING NULL,
 `scope_name` STRING NULL,
 `scope_version` STRING NULL,
 `service_name` STRING NULL SKIPPING INDEX WITH(granularity = '10240', type = 'BLOOM'),
 `span_attributes.thread.name` STRING NULL,
 TIME INDEX (`timestamp`),
 PRIMARY KEY (`service_name`)
)
PARTITION ON COLUMNS (`trace_id`) (
 trace_id < '1',
 trace_id >= 'f',
 trace_id >= '1' AND trace_id < '2',
 trace_id >= '2' AND trace_id < '3',
 trace_id >= '3' AND trace_id < '4',
 trace_id >= '4' AND trace_id < '5',
 trace_id >= '5' AND trace_id < '6',
 trace_id >= '6' AND trace_id < '7',
 trace_id >= '7' AND trace_id < '8',
 trace_id >= '8' AND trace_id < '9',
 trace_id >= '9' AND trace_id < 'a',
 trace_id >= 'a' AND trace_id < 'b',
 trace_id >= 'b' AND trace_id < 'c',
 trace_id >= 'c' AND trace_id < 'd',
 trace_id >= 'd' AND trace_id < 'e',
 trace_id >= 'e' AND trace_id < 'f'
)
ENGINE=mito
WITH(
 append_mode = 'true',
 table_data_model = 'greptime_trace_v1',
 ttl = '2years'
)

The table opentelemetry_traces is partitioned on the trace_id column, which is a STRING type. The partitioning scheme defines 16 partitions based on the lexicographical order of trace_id values. This partitioning strategy aims to distribute traces across partitions based on the starting character of their trace_id. For example, traces with trace_id starting with '1' will fall into one partition, traces starting with '2' in another, and so on. Analyzing the table schema and partitioning scheme is the first crucial step in identifying why the query plan might not be honoring partitions. Understanding how the data is distributed and the conditions under which each partition is selected is key to optimizing query performance. Let's explore common causes for this issue and potential solutions.

Potential Causes and Solutions

Several factors could contribute to the query plan not honoring table partitions. Let's discuss some common causes and their corresponding solutions:

1. Data Type Mismatch

A frequent culprit is a mismatch between the data type used in the query's WHERE clause and the data type of the partition key column. In this case, trace_id is defined as a STRING. It is critical to ensure that the query uses the correct data type when filtering on this column. For instance, if the query mistakenly treats trace_id as an integer or another type, the database might not be able to correctly map the filter to the appropriate partition(s).

Solution:

Verify Data Types: Double-check that the data type used in the query's WHERE clause matches the data type of the trace_id column in the table schema. Ensure that trace_id is treated as a STRING in the query.

Explicit Type Casting: If there's any ambiguity or potential for implicit type conversion, explicitly cast the value in the query to STRING. For example:

SELECT trace_id, timestamp, service_name
FROM opentelemetry_traces
WHERE trace_id = CAST('5c5f055730a1a04b1644f9e664406462' AS STRING)
AND timestamp >= '2025-09-28 14:00:00'
AND timestamp < '2025-09-28 14:02:00';

2. Incorrect Partition Key Predicates

The partitioning scheme is defined by a set of predicates that specify the range of values for each partition. If the query's WHERE clause doesn't align with these predicates, the database might not be able to determine which partitions to scan, leading to a full table scan. In the provided example, the partitions are defined based on lexicographical ranges of trace_id. A poorly formed query might not provide the database with enough information to leverage these ranges.

Solution:

Review Partition Predicates: Carefully examine the partition predicates defined in the CREATE TABLE statement. Understand the ranges and conditions for each partition.
Align Query with Predicates: Ensure that the query's WHERE clause includes conditions that directly correspond to the partition predicates. For example, if the partitions are defined using trace_id >= 'a' AND trace_id < 'b', the query should include a similar condition when filtering on trace_id.
Use Range Queries: When querying a partitioned column, using range queries (e.g., BETWEEN, >=, <) can often help the database effectively prune partitions. For instance:
```
SELECT trace_id, timestamp, service_name
FROM opentelemetry_traces
WHERE trace_id >= '5c5f055730a1a04b1644f9e664406462' AND trace_id < '5c5f055730a1a04b1644f9e664406463'
AND timestamp >= '2025-09-28 14:00:00'
AND timestamp < '2025-09-28 14:02:00';
```
This type of query provides a clear range that the database can use to identify the relevant partitions.

3. Implicit Type Conversion Issues

Databases often perform implicit type conversions, which can sometimes lead to unexpected behavior. If the database implicitly converts the trace_id to a different data type during the query execution, it might not be able to use the partitioning scheme effectively. This is more likely to occur when comparing values of different types without explicit casting.

Solution:

Avoid Implicit Conversions: Whenever possible, avoid relying on implicit type conversions. Explicitly cast values to the correct data type to ensure that the database interprets the query as intended.
Consistent Data Types: Maintain consistency in data types between the table schema, the data being inserted, and the queries being executed. This reduces the chance of implicit conversions occurring.

4. Statistics and Metadata

Databases rely on statistics and metadata to optimize query execution. If these statistics are outdated or inaccurate, the query optimizer might make suboptimal decisions, such as choosing to scan all partitions instead of pruning them. This can happen after significant data ingestion or schema changes.

Solution:

Update Statistics: Regularly update the database statistics. GreptimeDB, like other databases, has commands or procedures to update statistics. Consult the GreptimeDB documentation for the specific commands to use.
Analyze Table: Some databases provide an ANALYZE TABLE command (or similar) that collects statistics about the table data. Run this command after major data loads or schema changes.

5. Query Complexity and Optimizer Limitations

Complex queries with multiple joins, subqueries, or complex WHERE clause conditions can sometimes confuse the query optimizer. The optimizer might not be able to fully understand the query's intent and may fall back to a less efficient execution plan that involves scanning all partitions. Additionally, some optimizers have limitations in how they handle partitioning with certain types of queries.

Solution:

Simplify Queries: Break down complex queries into smaller, more manageable parts. This can help the optimizer make better decisions.
Rewrite Queries: Experiment with different ways of writing the same query. Sometimes, a slight change in the query structure can significantly impact the execution plan.
Use Hints (If Available): Some databases provide query hints that allow you to guide the optimizer. Check the GreptimeDB documentation to see if hints are available and how to use them to influence partition pruning.

6. Bugs and Known Issues

In rare cases, the issue might be due to a bug in the database system itself. While less common, it's important to consider this possibility, especially if you've exhausted other troubleshooting steps.

Solution:

Check Database Version: Ensure you are using the latest stable version of GreptimeDB. Bug fixes and performance improvements are often included in newer releases.
Consult Documentation and Forums: Review the GreptimeDB documentation, release notes, and community forums for any known issues related to partitioning and query optimization.
Report the Issue: If you suspect a bug, report it to the GreptimeDB development team with detailed information about your setup, query, and the observed behavior. This helps the developers identify and fix the issue.

Applying Solutions to the Example Query

Let's apply these potential solutions to the example query provided:

EXPLAIN SELECT trace_id,timestamp,service_name
FROM opentelemetry_traces
WHERE trace_id='5c5f055730a1a04b1644f9e664406462'
AND timestamp >= '2025-09-28 14:00:00'
AND timestamp < '2025-09-28 14:02:00';

Based on the partitioning scheme, the trace_id '5c5f055730a1a04b1644f9e664406462' should fall into a specific partition. To ensure partition pruning, we can try the following:

Verify Data Type: Ensure that the trace_id in the query is treated as a string. It already is in this case, but it's good to double-check.

Use Range Query (if appropriate): Since the partitions are defined based on ranges, we can rewrite the query to use a range condition:

EXPLAIN SELECT trace_id, timestamp, service_name
FROM opentelemetry_traces
WHERE trace_id >= '5c5f055730a1a04b1644f9e664406462' AND trace_id < '5c5f055730a1a04b1644f9e664406463'
AND timestamp >= '2025-09-28 14:00:00'
AND timestamp < '2025-09-28 14:02:00';

This assumes that 5c5f055730a1a04b1644f9e664406463 is the next possible value for trace_id in the lexicographical order.

Update Statistics: If the issue persists, consider updating the table statistics to ensure the optimizer has the most accurate information.

By applying these solutions and re-examining the EXPLAIN output, you can determine whether the query plan is now honoring the table partitions.

Conclusion

Ensuring that the query plan honors table partitions is essential for optimizing query performance in GreptimeDB and other time-series databases. By understanding the potential causes, such as data type mismatches, incorrect partition key predicates, outdated statistics, and query complexity, you can effectively troubleshoot and resolve issues related to partition pruning. Remember to analyze the table schema, review the partitioning scheme, and apply the appropriate solutions to achieve optimal query performance.

For further information on optimizing queries in GreptimeDB, consider exploring the official GreptimeDB documentation and community resources. You can also find valuable insights on database query optimization in general from resources like the PostgreSQL Wiki on performance tuning, which offers a comprehensive overview of database optimization techniques that are broadly applicable.