Deploy MeiliSearch with Dokku for production

Run open-source, fast and typo-tolerant search-engine with modern open-source PaaS

Deploy MeiliSearch with Dokku for production

I'll show you how to create and setup Dokku app for MeiliSearch deploying and how I use it with Python.

I use MeiliSearch in one of my Telegram bots, where I expect a search query from a user (with typos of course). Since I deploy everything (including Telegram bots) with Dokku, I found a way how can I deploy MeiliSearch as well.

Step-by-step deployment with Dokku

I assume you already have Dokku installed. Otherwise, check this tutorial.

Create Dokku app and attach domain you'll use in production:

dokku apps:create ms
dokku domains:set ms ms.okhlopkov.com

MeiliSearch requires some environment variables to run in production. MEILI_MASTER_KEY is the token (password) you'll use to access your service. I use these values:

dokku config:set ms \
	MEILI_ENV=production \
	MEILI_MASTER_KEY=followmeontwitter \
	MEILI_NO_ANALYTICS=true \
	MEILI_NO_SENTRY=true

MeiliSearch stores all the data it needs in data.ms file so we need to save it outside of the docker image for persistence.

dokku storage:mount ms /root/data.ms:/data.ms

Now everything is set up, we can start to deploy the MeiliSearch from the official Docker image and proxy its 7700 port to 80:

dokku git:from-image ms getmeili/meilisearch
dokku proxy:ports-set ghost http:80:7700

Then you can add Letsencrypt if you like:

dokku letsencrypt:enable ms

Or add Cloudflare certificates if you prefer (read the instruction here):

dokku certs:add ms < certs/okhlopkov.com.tar
dokku proxy:build-config ms

If you want to update MeiliSearch, just deploy from image again:

dokku git:from-image ms getmeili/meilisearch

How to use MeiliSearch

I'll show you how to add data and send search queries using Python. This is how I do it inside my Telegram bot:

MS_URL = "https://ms.okhlopkov.com/"
MS_SECRET = "followmeontwitter"

import meilisearch
client = meilisearch.Client(MS_URL, apiKey=MS_SECRET)
index = client.index('crunchbase_orgs')

Imagine that you prepared the list of objects dictionaries that you'd like to index. Now you need to upload your data to MeiliSearch. I'd suggest to batch upload your data: check out the snippet:

from tqdm.notebook import tqdm

def chunks(lst, n):
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        yield lst[i:i + n]

DATA_TO_UPLOAD = [{ your data here }, { and here }, ....]

chunk = 1000
for i, rch in tqdm(
    enumerate(chunks(res, chunk)),
    total=int(len(DATA_TO_UPLOAD) / chunk),
):
    upd = index.add_documents(rch)

⚠️ Important: if you have 100k-1M data rows, it will require ~4-8 hours to index them all. Yes, MeiliSearch has a fast search but a slow insert. And it's ok.

After you insert data to the index, you can observe its stats:

After all indexing is done, you can start to query your data:


Got questions? Please ask them on Twitter.