Question 1

Does TextQL optimize queries for ClickHouse's columnar storage?

Accepted Answer

Yes. Ana generates queries specifically optimized for ClickHouse's column-oriented architecture. It selects only the columns needed, applies partition key filters for time-based queries, and respects your table's ORDER BY key for efficient data scanning. This ensures consistently fast performance even on tables with billions of rows.

Question 2

Can TextQL query ClickHouse materialized views?

Accepted Answer

Yes. TextQL discovers and queries materialized views alongside regular tables. When a materialized view contains pre-aggregated data that's relevant to your question, Ana prefers it over scanning the raw table — giving you faster answers with lower resource consumption.

Question 3

Does TextQL work with ClickHouse Cloud?

Accepted Answer

Yes. TextQL supports ClickHouse Cloud, self-hosted ClickHouse, and Altinity-managed deployments. For ClickHouse Cloud, simply enter your cloud service endpoint and credentials. The connection, schema discovery, and query generation work identically across all deployment types.

Question 4

Can I analyze real-time streaming data with TextQL?

Accepted Answer

Yes. If your ClickHouse tables are receiving real-time inserts from Kafka, or other streaming sources, TextQL queries the latest data available. Ana can answer questions about events that occurred seconds ago — ideal for monitoring, alerting investigations, and live operational analytics.

Question 5

How does TextQL handle ClickHouse's different table engines?

Accepted Answer

TextQL reads the table engine type (MergeTree, ReplacingMergeTree, AggregatingMergeTree, etc.) from system tables and adapts its query generation accordingly. For example, Ana applies FINAL modifiers for ReplacingMergeTree tables and uses appropriate aggregation functions for AggregatingMergeTree.