Remove batch processor from traces pipeline

[dineshg13]

What does this PR do?

The PR removes batch processor from OTLP traces pipeline.

Motivation

OTLP trace data in the agent goes through multiple hops before it is sent to our backend. Agent drops trace data during the handshake and customer receives error with no helpful information and no way to overcome the limitations.

Customers can set the OTLP GRPC receiver message limit via max_rcv_msg_size_mib. The batch processor will combine large GRPC messages and send them to the trace agent. It results in a message that is larger than the trace agent GRPC limit of 10 MB. We can't configure the message size via the batch processor; it results in large messages that get dropped because of the trace agent limit.

For example, If we send five messages of 3 MB size to the core agent within 10 seconds. Core Agent will try to send a 15 MB message to the trace agent, but that fails.

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Use the python script to generate traces.

from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import \
    OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.trace import SpanKind

trace.set_tracer_provider(TracerProvider())

# Configure the OTLP exporter
otlp_exporter = OTLPSpanExporter(
    endpoint="localhost:43177",
    insecure=True,
)

# Add the OTLP exporter to the tracer provider
trace.get_tracer_provider().add_span_processor(BatchSpanProcessor(otlp_exporter))


# Function to generate a single trace with multiple spans
def generate_single_trace_with_multiple_spans(
    tracer, child_spans_count, attributes_count, attribute_length
):
    with tracer.start_as_current_span("root-span", kind=SpanKind.SERVER) as root_span:
        # Creating child spans within the root span
        for i in range(child_spans_count):
            with tracer.start_as_current_span(
                f"child-span-{i}", kind=SpanKind.INTERNAL
            ) as child_span:
                # Add specified number of attributes to each child span
                for j in range(attributes_count):
                    attr_value = (
                        "x" * attribute_length
                    )  # Create an attribute value of the specified length
                    child_span.set_attribute(f"key_{j}", attr_value)


# Get a tracer
tracer = trace.get_tracer(__name__)

# Generate a trace with multiple spans and add attributes to each span
generate_single_trace_with_multiple_spans(
    tracer, child_spans_count=1000, attributes_count=50, attribute_length=100
)

Run the script in a loop

for i in {1..100}                                                                                                                                                                                                     
do
  python py_apps/generate_trace.py
done

With the change, the data shouldn't get dropped in core agent. We should see the trace in the Datadog UI.

Reviewer's Checklist

• [x] If known, an appropriate milestone has been selected; otherwise the Triage milestone is set.
• [ ] Use the major_change label if your change either has a major impact on the code base, is impacting multiple teams or is changing important well-established internals of the Agent. This label will be use during QA to make sure each team pay extra attention to the changed behavior. For any customer facing change use a releasenote.
• [ ] A release note has been added or the changelog/no-changelog label has been applied.
• [ ] Changed code has automated tests for its functionality.
• [x] Adequate QA/testing plan information is provided. Except if the qa/skip-qa label, with required either qa/done or qa/no-code-change labels, are applied.
• [x] At least one team/.. label has been applied, indicating the team(s) that should QA this change.
• [ ] If applicable, docs team has been notified or an issue has been opened on the documentation repo.
• [ ] If applicable, the need-change/operator and need-change/helm labels have been applied.
• [ ] If applicable, the k8s/<min-version> label, indicating the lowest Kubernetes version compatible with this feature.
• [ ] If applicable, the config template has been updated.

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: 94a01a3c-4331-400f-9e8a-aedd8f4c6bfd Baseline: 4bf26c276f788b974939b86c00cda694b907007e Comparison: ea8bfbdddcd45e66365486a76564e17bae795b75 Total CPUs: 7

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

Experiments with missing or malformed data

• basic_py_check

Usually, this warning means that there is no usable optimization goal data for that experiment, which could be a result of misconfiguration.

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	-1.26	[-7.82, +5.29]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	tcp_syslog_to_blackhole	ingress throughput	+1.19	[+1.14, +1.25]
➖	process_agent_standard_check	memory utilization	+0.22	[+0.18, +0.25]
➖	idle	memory utilization	+0.12	[+0.09, +0.15]
➖	file_tree	memory utilization	+0.08	[+0.00, +0.16]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.00, +0.00]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.01	[-0.02, +0.01]
➖	trace_agent_msgpack	ingress throughput	-0.02	[-0.03, -0.01]
➖	process_agent_real_time_mode	memory utilization	-0.05	[-0.09, -0.02]
➖	process_agent_standard_check_with_stats	memory utilization	-0.09	[-0.13, -0.06]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.12	[-1.55, +1.32]
➖	otel_to_otel_logs	ingress throughput	-0.63	[-1.24, -0.01]
➖	file_to_blackhole	% cpu utilization	-1.26	[-7.82, +5.29]

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

[dineshg13]

/merge

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