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| <!-- START doctoc generated TOC please keep comment here to allow auto update --> | ||
| <!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE --> | ||
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| - [Going to production with CTL](#going-to-production-with-ctl) | ||
| - [Setup security](#setup-security) | ||
| - [Achieving best performance](#achieving-best-performance) | ||
| - [Balancing](#balancing) | ||
| - [Monitoring](#monitoring) | ||
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| <!-- END doctoc generated TOC please keep comment here to allow auto update --> | ||
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| # Going to production with CTL | ||
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| ## Setup security | ||
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| Ensure that: | ||
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| - You have thoroughly tested your dApp. Your tests should be based on security properties you want to maintain (who should/shouldn't be able to do X? under what conditions?). Write these properties down and review them as well. | ||
| - There are no private keys in the bundles | ||
| - Your private keys are stored securely (e.g. not served by the webserver) | ||
| - If you have private keys that are used specifically to control/shut down the system in case of a disaster, you are not storing them on the same server as the app. | ||
| - Blockfrost keys are not exposed to the user. If you run with Blockfrost, you have to set up a reverse proxy that attaches the project ID header. | ||
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| ## Achieving best performance | ||
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| Most likely, you will need a custom query layer to scale your dApp. Kupo is not very performant when it comes to handling a lot of queries on mainnet if it is configured to index the whole blockchain: [it has a slightly different purpose](https://github.com/CardanoSolutions/kupo/issues/146#issuecomment-1810571796). | ||
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| The best approach would be to maintain your dApp state by folding incoming transactions (while handling rollbacks). Consider using [Ogmios chain-sync API](https://ogmios.dev/mini-protocols/local-chain-sync/). | ||
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| [Here's how to plug your own query layer as a "provider" into CTL](./custom-query-layers.md). | ||
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| ## Balancing | ||
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| Use multiple backend instances if you have a lot of users, but avoid switching between different instances for a single user: CTL mildly relies on consistency of the state. | ||
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| How many instances to run? Compute the number based on the response times of your query layer and expected number of users. | ||
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| ## Monitoring | ||
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| Set up monitoring and alerts for your backend services. | ||
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| Ensure that you actively check the following: | ||
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| - Disk space | ||
| - RAM usage | ||
| - Rate of failed requests to the query layer backend (e.g. kupo returns 503 when overloaded) | ||
| - Average response time | ||
| - Usage limits for Blockfrost |