#community-help

Improving Typesense Query Performance

TLDR Jonathan queried about slower than expected typesense query performance. Jason and Kishore Nallan offered solutions and explanations. After a series of tests, Jonathan found other queries returned results quickly, indicating the issue was specific to the original query.

11mo

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Nov 08, 2022 (11 months ago)

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Jonathan

07:05 PM

I've been experimenting with various search libs (sonic, meili, typesense, etc.), so far typesense is the easiest to get working with raw hex strings (20 byte blockchain addresses encoded as hex), but the query performance is slower than expected:

i've indexed 50 million items as a test, each one looks something like:

{"dbid": 1337, "address": "64a43130af34f9150030f2a2509a9efbd07fe372"}

querying for "000000" returns 4 items in ~200ms (12 cores, 128gb ram, 4x2TB RAID 0)

200ms is pretty decent, but not "amazing". an in-memory ART (adaptive radix trie, which i believe typesense also uses) can return this in a few ms. does 200ms seem in-line with your expectations?

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Jason

07:07 PM

Could you share all the search query params you’re using?

07:07

Jason

07:07 PM

And also the exact collection schema?

Jonathan

07:08 PM

QUERY:

curl ""

SCHEMA:

curl "" \
  -X POST \
  -H "Content-Type: application/json" '{
    "name": "addresses",
    "fields": [
      {"name": "dbid", "type": "int64" },
      {"name": "address", "type": "string" }
    ],
    "default_sorting_field": "dbid"
  }'

07:11

Jonathan

07:11 PM

(also running via docker label 0.24.0.rcn28, i meant to try outside of docker but haven't yet)

Jason

07:12 PM

Could you try adding these additional search params:

num_typos=0 &amp; typo_tokens_threshold=0 &amp; drop_tokens_threshold=0 &amp; prioritize_exact_match=false &amp; highlight_fields=none

(space added for readability)

and see if that makes a difference performance-wise

07:13

Jason

07:13 PM

I’ve anecdotally seen slower performance when run via Docker… But could you make sure that the Docker runtime is allowed to use all the cores and memory on the host machine?

07:13

Jason

07:13 PM

If that also doesn’t help, could you check if running natively on the host makes a difference?

Jonathan

07:15 PM

adding those fields did not change performance at all, testing docker stuff now

07:52

Jonathan

07:52 PM

from 200ms (docker) to 280ms (running on host directly), typesense-server-0.23.1-linux-amd64.tar.gz

that's with a fresh data directory, new index, and restart after creating index. surprising result

Jason

07:53 PM

Hmmm! That was unexpected

07:54

Jason

07:54 PM

Kishore Nallan Any idea what’s happening here ^

07:55

Jason

07:55 PM

Btw, could you post the output of GET /metrics.json?

Jonathan

07:56 PM

{
  "system_cpu10_active_percentage": "0.00",
  "system_cpu11_active_percentage": "9.09",
  "system_cpu12_active_percentage": "0.00",
  "system_cpu13_active_percentage": "9.09",
  "system_cpu14_active_percentage": "0.00",
  "system_cpu15_active_percentage": "9.09",
  "system_cpu16_active_percentage": "0.00",
  "system_cpu17_active_percentage": "10.00",
  "system_cpu18_active_percentage": "0.00",
  "system_cpu19_active_percentage": "9.09",
  "system_cpu1_active_percentage": "27.27",
  "system_cpu20_active_percentage": "0.00",
  "system_cpu21_active_percentage": "0.00",
  "system_cpu22_active_percentage": "0.00",
  "system_cpu23_active_percentage": "0.00",
  "system_cpu24_active_percentage": "0.00",
  "system_cpu2_active_percentage": "25.00",
  "system_cpu3_active_percentage": "10.00",
  "system_cpu4_active_percentage": "10.00",
  "system_cpu5_active_percentage": "0.00",
  "system_cpu6_active_percentage": "9.09",
  "system_cpu7_active_percentage": "9.09",
  "system_cpu8_active_percentage": "9.09",
  "system_cpu9_active_percentage": "0.00",
  "system_cpu_active_percentage": "6.10",
  "system_disk_total_bytes": "7610737090560",
  "system_disk_used_bytes": "3837115981824",
  "system_memory_total_bytes": "134997864448",
  "system_memory_used_bytes": "71718522880",
  "system_network_received_bytes": "0",
  "system_network_sent_bytes": "0",
  "typesense_memory_active_bytes": "11111964672",
  "typesense_memory_allocated_bytes": "11072338904",
  "typesense_memory_fragmentation_ratio": "0.00",
  "typesense_memory_mapped_bytes": "11397263360",
  "typesense_memory_metadata_bytes": "226870128",
  "typesense_memory_resident_bytes": "11111964672",
  "typesense_memory_retained_bytes": "1533775872"
}

Jason

07:58 PM

Is the 200ms specific to the query 000000? Could you try a random set of other strings to see if it’s consistent?

Jonathan

07:58 PM

very good q

07:59

Jonathan

07:59 PM

oh wow, that may be it

07:59

Jonathan

07:59 PM

wtf

08:01

Jonathan

08:01 PM

all other queries are coming back in 0ms, lightning fast

Jason

08:01 PM

😅

Jonathan

08:01 PM

wow

08:01

Jonathan

08:01 PM

beautiful

08:01

Jonathan

08:01 PM

you made my day

08:01

Jonathan

08:01 PM

thank you

Nov 09, 2022 (11 months ago)

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Kishore Nallan

01:38 AM

There's a bunch of stuff we do with prefix searching that is not as straightforward as simply using an ART index directly. For e.g. we also sort words that match a prefix based on their frequency/popularity of occurrence. So certain popular prefixes could be a bit slower.

Jonathan

01:50 AM

(this particular prefix, 00000 only had 4 matches in 50 million documents so it may not be due to that but i acknowledge your point)

i'm surprised and impressed that raw hex strings worked so well with typesense. most other search libraries couldn't handle it

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