pg full text search very slow for Chinese characters

Hi Team,

Can anyone shed some light on why postgres 11 is extremely slow in my case?

I am making a mirror of zh.wikisource.org and I have downloaded 303049 pages and stored them in a postgres 11 database.

My postgres instance is based on docker image postgres:11 and runs on my MacBook Pro i7 16GB.

Database schema is as follows

Table pages(id, url, html, downloaded, inserted_at, updated_at) and books(id, name, info, preface, text, html, url, parent_id, inserted_at, updated_at, info_html, preface_html)

A wikisource web page is downloaded and its html text is inserted into table “pages” column “html.
Later, books.{name, info, preface, text, html, info_html, preface_html} are extracted from pages.html. The text column of books is a txt version of the content of html column of table pages.

On average there are 7635 characters (each characters is 3 bytes long because of utf-8 encoding) for text column of table books and I want to add full text search to books(text).

I tried pg_trgm and my own customized token parser https://github.com/huangjimmy/pg_cjk_parser

To my surprise, postgres 11 is extremely slow when creating a full text index.

I added a column of tsvector type and tried to create an index on that column. Pg could not finish creating a GIN index for a long time and I had to cancel the execution.
I then tried to create a partial full text index for 500 rows and it took postgres 2 to 3 minutes to create the index. Based on this estimation, pg will need at least one day to create a full GIN full text search index for 303049 rows of data. I think this is ridiculous slow.
If I tried to create fts index for books(name) or books(info), it took just 3 minutes to create the index. However, name and info are extremely short compared to books(text).

I switched to Elasticsearch and it turned out that Elasticsearch is extremely efficient for my case. It took Elasticsearch 3 hours to index all 303049 rows.

Jimmy Huang
jimmy_huang(at)live(dot)com