# Deploy MeiliSearch with Dokku for production

# Deploy MeiliSearch with Dokku for production

> Deploy MeiliSearch with Dokku in production: app setup, domain, master key, persistent storage, Docker deployment, Python indexing example, and security notes.

Source: https://okhlopkov.com/deploy-meilisearch-with-dokku-for-production/

Quick answer: MeiliSearch works well on Dokku when you treat it as a stateful service: set a master key, mount persistent storage, put it behind HTTPS, and keep indexing code separate from the public search endpoint. The risky parts are secrets, backups, and accidental open admin APIs. Use a strong master key and never expose it to the browser. Persist the data directory before importing documents. Plan backups before the search index becomes production-critical. I'll show you how to create and setup Dokku app for MeiliSearch deploying and how I use it with Python. Dokku is a free open-source self-hosted Heroku alternative which I use in all my production environments. 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 ms 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 . FAQ Can MeiliSearch run as a simple Dokku app? Yes, but only if you persist the data directory and configure the master key. Otherwise a redeploy or dyno reset can wipe the index. Should the frontend call MeiliSearch directly? Only with a restricted search key. Admin keys and indexing keys should stay server-side.