Rails postgres json query8/22/2023 In a YugabyteDB Friday Tech Talk (YFTT), I discussed these techniques and highlighted the significance of range sharding for distributed SQL databases. However, if the filter returns most of the table’s row, a full table scan (Seq Scan) is probably the most efficient. The mentioned indexes are designed to prevent full table scans and contribute to improved performance so that the response time isn’t directly tied to the total number of rows in the table. Understanding the time complexity and scalability of the execution plan is crucial. Performance may vary if you utilize a different storage system, like YugabyteDB, that doesn’t necessitate vacuuming or employ bitmap scan. Response time can fluctuate due to factors like query patterns, cached data, concurrent workload, and database system configuration.įor instance, when working with PostgreSQL, or a PostgreSQL-compatible database, the response time may be influenced by vacuuming, the utilization of a GIN index with bitmap scan, and the number of Rows Removed by Index Recheck. To evaluate performance and scalability, I prioritize running small reproducible examples and analyzing the execution plan rather than relying solely on response time. Index Scan using words_word_trgm on words (actual time=12.604.119.505 rows=56 loops=1)Ī GIN index on trigrams is useful for generic text search, but if all you need is a prefix, a direct index will be more efficient. It can show more false positive (There’s still work in progress to optimize it-see Github issue #7850): yugabyte=> explain (analyze, costs off, summary off) Because YugabyteDB distributes rows and not pages, it doesn’t do any bitmap scans. There are only three false positives to discard after the index scan.īitmap scan works on table pages. > Bitmap Index Scan on words_word_trgm (actual time=0.854.0.854 rows=9 loops=1) Recheck Cond: (word ~~ 're%olution'::text) Select word from words where word like 're%olution' īitmap Heap Scan on words (actual time=0.872.0.879 rows=6 loops=1) Index Cond: ((word >= 'data'::text) AND (word = '-'::text) AND (word = '-PH'::text) AND (upper(word) = '-ph'::text) AND (upper(word) = 'msi'::text) AND (reverse(word) explain (analyze, costs off, summary off) Index Only Scan using words_word on words (actual time=6.632.6.643 rows=28 loops=1) Select word from words where word like 'data%' yugabyte=# explain (analyze, costs off, summary off) However, for range queries, the sharding must be done by range of value, which must be defined with an explicit ASC or DESC because hash sharding modifies the order and a Seq Scan would be the only solution. The default sharding method with YugabyteDB applies a hash function on the first column of the index which allows YugabyteDB to distribute and provide scalable point queries. ![]() YugabyteDB re-uses PostgreSQL for the query layer, with additional sharding options to distribute and replicate for scalability and high availability. In PostgreSQL, the asc option is not mandatory because it’s the default. yugabyte=# create index words_word on words ( word asc ) If you know the prefix, indexing for LIKE is easy because PostgreSQL can transform it to a range scan an ascending or descending index provides fast and scalable access. Only some details about the storage implementation are different in the execution plan. The examples provided here work the same on the PostgreSQL database. Please note that I will run the demos on YugabyteDB, which is PostgreSQL-compatible. Yugabyte=# \copy words(word) from '/usr/share/dict/words' Id bigint generated always as identity primary key ![]() I’ll use a simple dataset, the Linux dictionary of English words installed in the dict package: yugabyte=# \! sudo dnf install -y words Let’s take a look to see how, for situations where the wildcard is either in front or at the end of the pattern. With the LIKE operator, you can compare a string to a pattern that includes some wildcards and then index for these access patterns.
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