jmespath.py VS GJSON

The Azure CLI lets us write queries to filter the results of the az storage blob list command. The queries are written in JMESPath, which is a query language for JSON. In this case, we are filtering the results to only include blobs that end with the .bacpac extension and then selecting the first one as ordered by the lastModified property. If there are no blobs found, the script exits with a failure code. If we find a blob, we download it to the local path specified by the localPath variable.

For JSON there is the `jmespath` library which might help.

https://github.com/jmespath/jmespath.py

I love jq, but I also use JMESPath (especially with AWS CLI), yq (bundled with tomlq and xq as well), and dasel [2]. I also wish hclq [3] wasn't so dead!

[0]: https://jmespath.org/

Probably writing the query side of things is a lot of the fun here, but there is actually a spec (and a complying Rust impl) you can hook into for this JQ-like querying: https://jmespath.org/ ( https://github.com/jmespath/jmespath.rs ).

Thanks to custom variables that use labels, we can create various filters for the dashboard. You can look up my configuration of variables and extend it with an analogy way for your own needs. At the top, I marked the filter with detected pods in selected namespace. In the lower part, you can see a preview of all labels that are associated with a single log line. Most labels are meta information that Promtail adds during scraping targets. This part of the Promtail configuration provides it. In this section, I also marked a few labels that not comes out-of-the box e.g. leavel , class , thread . We added these labels using the Promtail json stage. You need to know that Promtail processes scraped logs in a pipeline. A pipeline is comprised of a set of stages. json stage is a parsing stage that reads the log line as JSON and accepts JMESPath expressions to extract data.

The tester on the official website for JMESPath (what json_query is doing) has been useful to me: https://jmespath.org/

Interesting. How does this compared to Jmespath? Not saying Jmespath is superior, just wondering whether you were aware of it.

Someone made a benchmark of serialization libraries in go [1], and I was surprised to see gobs is one of the slowest ones, specially for decoding. I suspect part of the reason is that the API doesn't not allow reusing decoders [2]. From my explorations it seems like both JSON [3], message-pack [4] and CBOR [5] are better alternatives.

By the way, in Go there are a like a million JSON encoders because a lot of things in the std library are not really coded for maximum performance but more for easy of usage, it seems. Perhaps this is the right balance for certain things (ex: the http library, see [6]).

There are also a bunch of libraries that allow you to modify a JSON file "in place", without having to fully deserialize into structs (ex: GJSON/SJSON [7] [8]). This sounds very convenient and more efficient that fully de/serializing if we just need to change the data a little.

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1: https://github.com/alecthomas/go_serialization_benchmarks

2: https://github.com/golang/go/issues/29766#issuecomment-45492...

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3: https://github.com/goccy/go-json

4: https://github.com/vmihailenco/msgpack

5: https://github.com/fxamacker/cbor

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6: https://github.com/valyala/fasthttp#faq

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7: https://github.com/tidwall/gjson

8: https://github.com/tidwall/sjson

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I don't think there is a way to sort an array, though. However, there is an option to have keys sorted. Personally, I don't think there is much annoyance in that. One could just pipe `jj` output to `sort | uniq -c`.

[0]: https://github.com/tidwall/gjson/blob/master/SYNTAX.md

I’m using GJSON, so far so good!

If the fields you need are just a small subset of the whole json, maybe https://github.com/tidwall/gjson might be of use to read only those (using jsonpath) without needing to create complete corresponding structs.

Thank you for the reminder, it's never too much of it :) Didn't say it, but the code was pprof-iled many times and i can really say it's well optimized. I use own libraries with on-the-fly equations (sums, avgs, emas, stds, ...) wherever possible and also made custom json parser as json messages are in fixed format, so the parser is about 10x faster than gjson. I optimized it to the point that I avoided using maps, and rather iterate via slice where ever possible.

You are right, for learning purposes this fit my needs, but I can imagine an approach similar to this repo: https://github.com/tidwall/gjson

https://github.com/tidwall/gjson is a relatively sensible library if this is something you need to deal with and the structure is actually unknowable.