RequestHunt is a community-driven platform that collects and curates feature requests from X (Twitter), Reddit, and GitHub. We help product teams discover what features real users are asking for, making it easier to validate product ideas and prioritize feature development based on actual user demand.

How does RequestHunt collect feature requests?

RequestHunt uses AI-powered extraction to automatically collect feature requests from social media platforms including X, Reddit, and GitHub. The platform monitors discussions, issues, and posts to identify genuine user feature requests, extracting key information like the requested feature, product mentioned, and user sentiment.

Who uses RequestHunt?

RequestHunt is used by product managers, founders, developers, and product teams who want to discover what features real users are requesting. It's particularly valuable for startups looking to validate ideas, established companies prioritizing their roadmap, and developers building products that solve real user problems.

Is RequestHunt free to use?

Yes, browsing and searching feature requests on RequestHunt is completely free. We also offer API access for developers who want to integrate feature request data into their own applications. Check our pricing page for API plan details.

How often is RequestHunt data updated?

RequestHunt continuously collects feature requests from Reddit, X (Twitter), GitHub, YouTube, LinkedIn, and Amazon. You can also trigger on-demand scraping via the API or web interface to get the freshest data for any topic.

[ClickHouse] Optimize inverse dictionary lookup for IN conditions

When using dictGet with equivalence filtering, the optimization works, but it does not work when filtering is used by a list (IN). This feature would ensure that the optimization is consistent across different filtering methods.

Original Source

@@handgunman | 0 pts | 3/14/2026

### Company or project name _No response_ ### Describe what's wrong When using dictGet in the request body with equivalence filtering, the optimization works. If filtering is used by a list (IN), then optimization does not work. ### Does it reproduce on the most recent release? Yes ### How to reproduce 26.1.4 click house-client `CREATE TABLE t1 (id Int32, val Int32) ENGINE = MergeTree() PARTITION BY id%100 ORDER BY id; CREATE TABLE t2 (id Int32, name String) ENGINE = MergeTree() ORDER BY id; INSERT INTO t1 SELECT number%100, number*100 FROM numbers(10000); INSERT INTO t2 SELECT number, 'S-'||toString(number) FROM numbers(100); CREATE DICTIONARY d (id Int32, name String) PRIMARY KEY id SOURCE(CLICKHOUSE(QUERY `SELECT id, name FROM t2`)) LAYOUT(HASHED) LIFETIME(MIN 300 MAX 3000); EXPLAIN indexes=1 SELECT t1.id, t1.val, dictGet('d','name',t1.id) as name FROM t1 WHERE name IN ('S-9') SETTINGS optimize_inverse_dictionary_lookup = 1 ;` https://fiddle.clickhouse.com/3b09a60

Discussion

Loading comments...