Version History

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Version 2.0.17 — 2026-07-18

  • Named user licensing enforced with a seat registry — the licensed user limit now counts named users instead of concurrent sessions. A licensed seat is assigned to a user on their first request and survives sign-outs, cache clears and server restarts; it is freed automatically after a period of inactivity defined by the license (30 days by default) or manually by an administrator. The License tab of the admin panel shows who occupies the seats, when each user was first and last seen, and provides a per-seat Release button. When all seats are taken, a new user gets a clear “Named user limit reached” message in Excel; existing users are unaffected. In multi-server deployments (CACHE_BACKEND: redis) the seat registry is shared by the whole cluster. See Admin panel.

  • Cache clearing API for ETL pipelines (POST /api/cache/clear) — an external system can now clear the XLTable cache right after updating the warehouse data, so users get fresh numbers immediately instead of waiting out the cache TTL. Authorization by a dedicated Bearer token (new API_TOKENS setting — no admin password in pipeline scripts) or by admin credentials. The scope parameter selects what is cleared: sql (default — shared query results only, sessions and cube metadata untouched), metadata (plus cube definitions, users stay signed in) or all. With CACHE_BACKEND: redis one call clears the whole cluster. See Cache management API.

  • SQL results are cached once and shared between users — previously query results were cached per user session, so a department opening the same dashboard sent the same SQL to the database once per user; now an identical SQL query is executed once per SQL_CACHE_TTL (600 seconds by default) and every other user gets the result from the cache within milliseconds. Excel Refresh keeps its meaning: the user who presses it bypasses the cached entries obtained before the refresh and — once the data is re-read — updates them for everyone; other users keep using the cache. Row-level security is unaffected: per-user access filters are part of the generated SQL text, so users with different permissions never share results. With CACHE_BACKEND: redis the cache is shared across all servers of the cluster. Cache size is capped (SQL_CACHE_MAX_MB, LRU eviction; SQL_CACHE_MAX_RESULT_MB per result), the feature can be turned off with SQL_CACHE_ENABLED: false, and the admin panel shows entry count, size and hit rate. See Refreshing data.

  • Query marker in database logs for all connectors — every SQL query now starts with a /* user:<name>, app:xltable */ comment (previously only ClickHouse tagged queries, via log_comment), so database administrators can find XLTable queries — and see which user requested the data — in the query history of any supported database: ClickHouse system.query_log, Greenplum pg_stat_activity/server log, StarRocks audit log, Trino system.runtime.queries, Snowflake QUERY_HISTORY, Databricks system.query.history, BigQuery INFORMATION_SCHEMA.JOBS. Ready-to-use history queries per database are in the FAQ. The ClickHouse log_comment marker is kept unchanged.

  • Query timeout for all connectors (query_timeout) — the query_timeout parameter in CREDENTIAL_DB (maximum execution time of a single database query, in seconds; 300 by default) is now honored by every connection type, not only ClickHouse. Where the database supports it, the query is cancelled server-side (Snowflake STATEMENT_TIMEOUT_IN_SECONDS, Databricks STATEMENT_TIMEOUT, Greenplum statement_timeout, Trino query_max_execution_time, StarRocks query_timeout, BigQuery job timeout); for embedded DuckDB the query is interrupted by XLTable itself. See Database connections.

  • Cancelling a running query works in all connectors — when a refresh is interrupted in Excel (pressing Esc), the server now cancels the query in the database instead of letting it run to completion. Previously only the ClickHouse connector supported this; now the query is cancelled server-side in Greenplum (pg_cancel_backend), StarRocks (KILL QUERY), Trino (system.runtime.kill_query), Snowflake (SYSTEM$CANCEL_QUERY) and BigQuery (job cancellation) — for these databases Cancel works across worker processes and servers, same as for ClickHouse. Databricks and embedded DuckDB have no server-side kill command, so the query is interrupted in-process: cancellation applies when the Cancel request reaches the same server process that runs the query.

  • Admin panel: SQL cache statistics reset — the Cache tab got a Reset stats button that starts the hit/miss counters of the shared SQL result cache from zero without touching the cached entries. The counters deliberately survive restarts and cache clearing, so the button is the way to measure the cache hit rate over a chosen period. See Admin panel.

  • Snowflake key-pair authentication — the Snowflake connector now supports the authentication method Snowflake recommends for service accounts after deprecating single-factor password sign-ins: specify private_key_path (and private_key_passphrase for an encrypted key) in CREDENTIAL_DB to authenticate with an RSA key pair. A programmatic access token (PAT) can still be passed in the password field, and existing password-based configurations keep working unchanged. See Database connections.

Version 2.0.16 — 2026-07-10

  • DuckDB Connector — added support for DuckDB as a data source. DuckDB is embedded: the whole database is a single file next to XLTable — no database server to install, which makes it the fastest way to try XLTable or to serve small and medium datasets. Cubes can also be built directly on top of Parquet/CSV files via read_parquet() / read_csv() in the cube definition. See DuckDB Sample Data.

  • Multi-server deployments (CACHE_BACKEND: redis) — several XLTable servers can now share one cache through Redis and work behind a load balancer (e.g. nginx) with no sticky sessions: a session opened through one server is valid on all of them, a running query can be cancelled through any server, the licensed user limit is counted across the whole cluster, and cache management in the admin panel of any server applies to all. The default single-machine cache is unchanged. See Scaling to multiple servers (Redis cache).

Version 2.0.15 — 2026-07-04

  • Parallel worker processes on Linux (concurrency) — the Ubuntu installer now starts several server processes behind nginx load balancing (one per CPU core, up to 4 by default; configurable with XLTABLE_INSTANCES). Heavy reports from many concurrent users are built in parallel instead of queueing on a single CPU core; all processes share one cache and one settings.json. Existing installations pick this up by re-running install_xltable.sh.

  • Worker threads and port settings (SERVER_THREADS, SERVER_PORT) — the number of worker threads of one server process is now configurable (default raised from 8 to 16, so more database queries stay in flight simultaneously), as is the listening port (SERVER_PORT, or the OLAP_PORT environment variable used by the multi-process setup).

  • Result cache size cap (RESULT_CACHE_MAX_MB) — query results larger than the configured size (16 MB by default) are no longer stored in the shared result cache: under concurrent load, writing huge cached responses made all worker processes queue on the cache database; such results are cheaper to rebuild. Also sped up the assembly of very large XMLA responses.

  • Collapse / Expand Entire Field — the Pivot Table context-menu commands Expand/Collapse → Collapse Entire Field and Expand Entire Field are now supported in all combinations, verified against SSAS traces, both for separate nested fields and for levels of a multi-level hierarchy: collapsing a whole nested field, expanding an entire field or hierarchy level (all items at once), expanding a single item of a collapsed field back (only that item shows the nested field) and collapsing single items of an expanded field. A collapsed field is not queried at all — its table is not scanned or joined until the field is expanded again.

  • Keep Only / Hide Selected Items — the Pivot Table context-menu commands Filter → Keep Only Selected Items and Hide Selected Items are now supported, including items of multi-level hierarchies. Excel’s service query for the hierarchy position of the selected items (__XlItemPath / __XlSiblingCount / __XlChildCount) is answered the same way as by SSAS.

  • Large dimension filters — Keep Only / Hide Selected Items works on dimensions with tens of thousands of members, verified against an SSAS trace with 20,000 items: the full member list is returned to Excel (capped by the new MAX_FILTER_MEMBERS setting, 100,000 by default), member filters are generated as compact SQL IN (...) lists instead of OR chains, and the ClickHouse connector automatically raises max_query_size for oversized queries (the default 256 KB parser limit rejected large filter lists).

  • Result limit in cells (MAX_CELLS) — the result size limit is now measured in cells of the pivoted table (row combinations × column combinations × measures) instead of rows, with a default of 1,000,000 — the same way SSAS limits oversized results. The legacy MAX_ROWS setting is still accepted. A separate, clear message is returned when the columns area exceeds the Excel sheet limit of 16,384 columns.

  • XMLA diagnostics (DUMP_XMLA) — new setting that dumps every raw XMLA request and response to the log folder, for diagnosing Excel/XMLA protocol issues.

  • Automatic log cleanup (LOG_RETENTION_DAYS) — log files older than the configured number of days (14 by default) are now removed automatically.

  • Overload protection (OVERLOAD_GUARD) — when the server host runs out of memory, CPU or disk space (configurable thresholds), data queries are rejected with a clear “Server is overloaded” message in Excel instead of being forwarded to the database. Metadata requests still pass, so cube connections stay alive.

  • Metadata cache TTL (METADATA_CACHE_TTL) — cached cube definitions, schema lists and query results now expire after a configurable period (600 seconds by default), so an edited cube is picked up automatically without clearing the cache.

  • Hot reload of settings.json — configuration changes are picked up automatically within a few seconds of saving the file, without a service restart. A file with a JSON syntax error is ignored (the previous configuration keeps working) and logged.

  • Cache follows settings.json — database credentials are no longer stored in the cache (they are read from the live configuration on every request), and the cache is cleared automatically whenever the configuration content changes — on hot reload or on service start. A service started with an incorrect configuration no longer requires a manual cache clear after the fix.

  • Authorization cache timeout (AUTH_CACHE_TIMEOUT) — cached authorizations of local users now expire the same way as Active Directory ones; the new setting applies to both (LDAP_CACHE_TIMEOUT is kept as a legacy fallback).

  • Admin panel: cache management — new per-user cache overview with last-activity times, a Sign out button for a single user, and a Clear Metadata Cache action that applies cube changes without signing users out.

Version 2.0.14 — 2026-07-01

  • Drillthrough — double-clicking a Pivot Table cell now returns the underlying detail rows. Detail columns are configured per measure group with the new olap_drillthrough tag in the cube definition.

  • New Jinja context — expanded the Jinja rendering context available in cube definitions with additional variables for building dynamic query logic.

  • Debug console — new debug console for inspecting rendered SQL, Jinja context and query execution, making cube development and troubleshooting easier.

Version 2.0.13 — 2026-06-18

  • Extended Jinja context — Jinja templates now receive additional context variables (user, now, request and per-source SQL fragments) for row-level security and dynamic query logic.

  • SQL and XML escaping — hardened escaping of user names, member values and identifiers across all connectors to prevent SQL injection and malformed XMLA responses.

  • Many-to-many fix — corrected SQL generation for many-to-many relationships.

  • Connector fixes — multiple fixes across database connectors, including ILIKE support for BigQuery.

  • MCP connector (in development) — early support for an MCP connector, currently under active development.

Version 2.0.12 — 2026-05-22

  • Greenplum Connector — added support for Greenplum as a data source.

  • HTTP gzip compression — server HTTP responses now support gzip compression, reducing the amount of data transferred.

  • Faster XMLA response building — significantly improved performance when generating XMLA responses with a large number of rows.

Version 2.0.11 — 2026-04-01

  • Databricks Connector — added support for Databricks as a data source.

  • OLAP cube syntax validation — new function for validating cube definition syntax before loading.

  • Windows 10 and 11 support — added the ability to run on Windows 10 and 11 operating systems.

Version 2.0.10 — 2026-03-19

  • StarRocks Connector — added support for StarRocks as a data source.

  • Admin Panel — new web-based interface for managing server configuration.

  • Part-source parameter — added part-source attribute for defining tag relationships in cube configuration.

  • Jinja context variables — cube definitions now support Jinja templating with context variables.

  • Improved logging — enhanced diagnostic output across all supported connectors for easier troubleshooting.