skeema VS jaeger

If a tool blindly drops columns, that's just a bad tool! It doesn't mean the concept is flawed.

Thousands of companies successfully use declarative schema management. Google and Facebook are two examples at a large scale, but it's equally beneficial at smaller scales too. As long as the workflow has sufficient guardrails, it's safe and it speeds up development time.

Some companies use it to auto-generate migrations (which are then reviewed/edited), while others use a fully declarative flow (no "migrations", but automated guardrails and human review).

I'm the author of Skeema (https://github.com/skeema/skeema) which has provided declarative flow for MySQL and MariaDB since 2016. Hundreds of companies use it, including GitHub, SendGrid, Cash App, Wix, Etsy, and many others you have likely heard of. Safety is the primary consideration throughout all of Skeema's design: https://www.skeema.io/docs/features/safety/

Meanwhile a few declarative solutions that support Postgres include sqldef, Migra, Tusker (which builds on Migra), and Atlas.

I’d argue the obvious answer is address the lack of great answers for declarative schema migration in PostgreSQL. There is Skeema https://github.com/skeema/skeema but it doesn’t support Postgres and Prisma iirc forces you into an ORM, atlas looks perfect but has a nonstandard license.

Ahh I see now, you've founded https://github.com/skeema/skeema which is great!

Keep it up!

Using tmpfs for MySQL/MariaDB's data directory helps tremendously. If you're using Docker natively on Linux, use `docker run --tmpfs /var/lib/mysql ...` and that'll do the trick. Only downside is each container restart is slightly slower due to having to re-init the database instance from scratch.

Tuning the database server settings can help a lot too. You can add overrides to the very end of your `docker run` command-line, so that they get sent as command-line args to the database server. For example, use --skip-performance-schema to avoid the overhead of performance_schema if you don't need it in your test/CI environment.

For MySQL 8 in particular, I've found a few additional options help quite a lot: --skip-innodb-adaptive-hash-index --innodb-log-writer-threads=off --skip-log-bin

A lot of other options may be workload-specific. My product Skeema [1] can optionally use ephemeral containerized databases [2] for testing DDL and linting database objects, so the workload is very DDL-heavy, which means the settings can be tuned pretty differently than a typical DML-based workload.

[1] https://github.com/skeema/skeema/

[2] https://www.skeema.io/docs/options/#workspace

Atlas hasn’t made it on my radar until now — surprising considering how many stars it has. Based on the description, it looks like it can do something similar to skeema except it isn’t limited to one flavor of sql like skeema. I’m looking forward to trying it out in my next postgres project.

VARCHAR(N) can store N characters. So with utf8mb3, that's a max of 3N bytes worst-case. But with utf8mb4, it's now 4N bytes, which (with a high N) may exceed internal limits such as maximum length of an index key.

IIRC, there were additional problems in older versions of MySQL, situations where sort buffers were sized to a fixed length equal to the value's worst-case size or something like that. So sorting a large number of utf8mb4 values would use a lot more memory than utf8mb3 values (again, iirc, I might be wrong on this).

So the safer and more backwards-compatible approach was to introduce utf8mb4 as a new separate charset, and allow users to choose. MySQL 8 is now transitioning towards deprecating utf8mb3, and will finally make the utf8 alias point to utf8mb4 sometime in the near future.

That said, there are still a bunch of unpleasant uses of utf8mb3 internally in things like information_schema. I develop schema management tooling and recently lost a week to some of the more obscure ones in https://github.com/skeema/skeema/commit/bf38edb :)

I realize my paradigm on schema driven projects comes probably from my background. I found a very similar tool by chance when reading through my latest feeds and found this tool: https://github.com/skeema/skeema

Jaeger maps the flow of requests and data as they traverse a distributed system. These requests may make calls to multiple services, which may introduce their own delays or errors. https://www.jaegertracing.io/

As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.

This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.

What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].

From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].

In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).

To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).

Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!

[1] https://github.com/highlight/highlight/blob/main/LICENSE

[2] https://www.jaegertracing.io

[3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...

[4] https://www.highlight.io/docs/getting-started/native-opentel...

[5] https://static.highlight.io/assets/docs/gorm.png

[6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...

[7] https://highlight.io/blog/clickhouse-materialized-views

[8] https://www.highlight.io/docs/getting-started/self-host/self...

[9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...

For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements

Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.

[1] https://www.jaegertracing.io/

From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.

Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.

However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.

In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.

The best way to do this, is with the help of tracing tools such as paid tools such as Honeycomb, or your own instance of the open source Jaeger offering, or perhaps Encore's built in tracing system.

In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.