feat(connectors): Feature/influxdb connector Sink and Source

[ryerraguntla]

Adds a native InfluxDB connector for Apache Iggy resolving #2700.

New crates:

• iggy_connector_influxdb_sink — consumes Iggy messages and writes them to InfluxDB 2.x via line protocol (/api/v2/write)
• iggy_connector_influxdb_source — polls InfluxDB Flux query results and produces messages to Iggy topics

Features:

• Batch writes / cursor-based polling
• Exponential backoff + jitter on retries
• Circuit breaker after consecutive failures
• HTTP 429 Retry-After header support
• Configurable precision (ns, us, ms, s)
• Full integration tests using Testcontainers (influxdb:2.7-alpine)

Closes #2700

What changed?

Implemented the following features based on a good connector framework
InfluxDB Sink Connector
Writes Iggy stream messages to InfluxDB v2 using the line protocol write API.
Data writing — Messages are serialised as line protocol with correct escaping for measurements, tag values, and string fields. Payloads can be written as JSON (validated and escaped), UTF-8 text, or Base64-encoded raw bytes. Timestamps are converted from Iggy's microsecond epoch to the configured InfluxDB precision (ns/us/ms/s, default µs). An offset-based nanosecond blend prevents silent deduplication when multiple messages in the same batch share the same microsecond timestamp. If a producer sets timestamp=0, the connector falls back to SystemTime::now() to avoid Year-1970 points.
Resilience — Writes are retried up to max_retries (default 3) on HTTP 429 and 5xx responses using exponential backoff with ±20% jitter. The Retry-After header is honoured in both integer-seconds and RFC 7231 HTTP-date formats before falling back to own backoff. A circuit breaker opens after circuit_breaker_threshold (default 5) consecutive batch failures and holds for circuit_breaker_cool_down (default 30s) before a half-open probe. Batch errors are captured and propagated to the runtime after processing all remaining sub-batches, preventing silent data loss.
Startup — open() retries the InfluxDB /health endpoint up to max_open_retries (default 10) with capped exponential backoff, so the connector recovers from transient InfluxDB restarts without manual intervention.
Metadata — Stream name, topic, and partition can be written as InfluxDB tags; message checksum and origin timestamp as fields. Each is individually togglable. The measurement name is configurable (default iggy_messages). Verbose logging mode promotes per-batch diagnostics from debug! to info! without code changes.

InfluxDB Source Connector
Polls InfluxDB v2 via the Flux query API and publishes results to Iggy topics as structured JSON messages.
Incremental polling — Tracks max(_time) from each Flux response as a cursor, advanced by 1 ns before storing to prevent boundary-point re-delivery. The cursor is templated into any $cursor and $limit placeholder in the user-supplied Flux query, supporting arbitrary range, filter, pivot, and aggregation chains. The cursor and row count are persisted via ConnectorState so polling resumes exactly where it left off after a restart.
CSV parsing — Handles InfluxDB annotated CSV output: skips #group, #datatype, and #default annotation rows, detects header rows by field names, and correctly handles blank-line separators between multi-table results.
Message format — Each row is published as a structured JSON envelope {measurement, field, timestamp, value, row} by default. When payload_column is set, the raw field value is extracted and emitted in the configured format (JSON, Text, or Raw/Base64).
Resilience — Same exponential backoff, jitter, Retry-After parsing, and circuit breaker as the sink. open() retries the /health endpoint before declaring ready. close() drops the reqwest client to release all connection pool resources.
Configuration — cursor_field (default _time) and initial_offset allow operators to replay from any point in the bucket. poll_interval, batch_size, and timeout are all tunable. Verbose logging toggles per-poll diagnostics between debug! and info!.

Local Execution

• Passed
• Pre-commit hooks ran

AI Usage

If AI tools were used, please answer:

1. Which tools? GitHub Copilot, Claude
2. Scope of usage? autocomplete, generated functions
3. How did you verify the generated code works correctly? - Yes Verified by testing
4. Can you explain every line of the code if asked? Yes

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 70.26%. Comparing base (3f651e0) to head (f86d30a).

Additional details and impacted files

@@             Coverage Diff              @@
##             master    #2933      +/-   ##
============================================
- Coverage     70.27%   70.26%   -0.02%     
  Complexity      862      862              
============================================
  Files          1028     1028              
  Lines         85279    85279              
  Branches      62656    62666      +10     
============================================
- Hits          59932    59923       -9     
  Misses        22833    22833              
- Partials       2514     2523       +9     

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csharp	67.43% <ø> (-0.19%)	⬇️
go	36.37% <ø> (ø)
java	59.87% <ø> (ø)
node	91.37% <ø> (-0.15%)	⬇️
python	81.43% <ø> (ø)

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[hubcio]

@ryerraguntla you said that precommit hooks were ran, yet clippy is failing. how is that possible? ;)