Input

Output

What is the Avro to YAML Converter?

You have an Apache Avro schema or a JSON-encoded Avro record, and you want it in YAML — usually because the Helm chart, Kubernetes ConfigMap, or pipeline config it is going into is YAML-flavored. This tool does that conversion in your browser. Paste the .avsc or record on the left, see the YAML on the right.

Avro schemas are valid JSON under the Avro 1.11 specification, and YAML is a strict superset of JSON per yaml.org, so the conversion is well-defined. Records become mappings, fields become keys, arrays become block sequences. Strings get quoted only when they contain special characters or look like reserved YAML scalars (true, null, yes, etc.) — everything else stays unquoted, which is what makes YAML readable.

No upload, no server. Parsing uses your browser's native JSON parser. Nothing leaves the page.

How to Use It

Three steps. The buttons described below are the ones actually on this page.

Paste, Upload, or Load a Sample

Drop an Avro schema or JSON-encoded Avro record into the left Input panel. Upload handles .avsc / .json files; Sample loads a realistic Order schema with line items, currency enum, and an optional address. Minified JSON looks like this:

{"type":"record","name":"Order","fields":[{"name":"orderId","type":"string"},{"name":"totalCents","type":"long"}]}

Both schemas (.avsc) and JSON-encoded records work — they are both JSON underneath.

Read the YAML Output

The right panel renders YAML with two-space indentation, the way YAML 1.2 intends. Output updates 300 ms after you stop typing — there is no Convert button to click.

Copy or Download

Copy grabs the YAML for a chat or PR. Download saves it as output.yaml. The trash icon clears the input. Conversion runs entirely on the client through JSON.parse() — your data never touches a network.

When You'd Actually Use This

Helm Charts and ConfigMaps

You have an Avro schema in a Schema Registry and need to embed it in a Helm values.yaml or a Kubernetes ConfigMap. Paste, copy the YAML, drop it into the chart.

CI Pipeline Definitions

GitHub Actions, GitLab CI, and most modern pipeline tools eat YAML. If your build step needs a sample Avro record as input, paste the JSON encoding here and grab the YAML version for the pipeline file.

Documentation that Reads Better

YAML reads better than JSON for humans — fewer braces, no trailing commas. When documenting an Avro schema for a teammate, the YAML version often goes into the wiki even when the source of truth is .avsc.

Cross-format Sanity Checks

Sometimes you spot a schema bug only after seeing the data shape laid out differently. Same record, different format, different lens — and the typo jumps out.

Common Questions

Does the YAML round-trip cleanly back to JSON?

Yes for the data shapes Avro produces — strings, numbers, booleans, nulls, arrays, and objects. The output is plain YAML 1.2, parseable by any compliant YAML library (PyYAML, snakeyaml, js-yaml, etc.). Tags, anchors, and aliases are not emitted because Avro JSON does not need them.

What about Avro-specific things like logical types?

Logical types ({"type": "long", "logicalType": "timestamp-millis"}) are just nested JSON objects in the schema, so they survive the conversion intact — they become a nested mapping in YAML. Same for unions, enums, fixed types, and namespaces.

Is my schema sent to a server?

No. Conversion runs entirely in your browser. Nothing is uploaded, nothing is logged. Close the tab and the data is gone.

Can I paste a binary .avro Object Container File?

Not directly. This converter takes JSON in (schema or record). For binary OCF files, run them through avro-tools tojson first, then paste the result.

Why are some strings quoted in the YAML output?

Strings get quoted when they contain commas, colons, hashes, or look like a reserved YAML scalar (true, false, null, yes, no). That is per YAML 1.2 rules — without the quotes, a parser might read "true" as a boolean instead of the string "true".

How big a schema can I paste?

Anything your browser can hold. Schemas with a few thousand fields are fine. Past 10 MB the Ace editor itself starts to lag — that is the bottleneck, not the converter.

Other Avro and YAML Tools

Once your schema is in YAML, these tools cover what comes next: