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Martin 2024-12-04 17:23:14 +08:00
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# Python-generated files
__pycache__/
*.py[oc]
build/
dist/
wheels/
*.egg-info
# Virtual environments
.venv
signoz/signoz_data

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.python-version Normal file
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3.13

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FROM python:3.12-slim-bookworm
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
# 禁用 venv
ENV UV_PROJECT_ENVIRONMENT="/usr/local/"
RUN --mount=type=cache,target=/var/cache/apt \
--mount=type=cache,target=/var/lib/apt \
sed -i 's/deb.debian.org/mirrors.ustc.edu.cn/g' /etc/apt/sources.list.d/debian.sources && \
apt-get update && \
apt-get install -y gcc g++ python3-dev
WORKDIR /app
RUN --mount=type=bind,source=./pyproject.toml,target=/app/pyproject.toml \
--mount=type=bind,source=./uv.lock,target=/app/uv.lock \
--mount=type=cache,target=/root/.cache/uv \
uv sync --frozen --no-install-project

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# ez-otlp
> ez-otlp is a simple tool that enables convenient use of modern monitoring systems with minimal configuration. It helps developers streamline telemetry data collection and transmission, making it ideal for services in modern cloud-native applications and microservices architectures. It efficiently collects logs, metrics, and traces via OpenTelemetry and seamlessly integrates with existing monitoring tools and platforms like Prometheus, Grafana, Datadog, and more.
🚀 Quickly implement application performance monitoring, log management, distributed tracing, metrics and dashboards, and alerts.
🛠️ 99% of parameter configurations are resolved through environment variables, making it plug-and-play and ready to seamlessly integrate with OpenTelemetry without the need for complex parameters.
❤️ The demo uses SigNoz for an intuitive interface that showcases the power of modern monitoring systems.
### Usage Instructions
##### In your project
```python
# env
# EZ_ENDPOINT = http://host.docker.internal:4317/v1/traces
# EZ_RESOURCE_SERVICE_NAME = ez-otlp
from ez_otlp import EZ_OTLP
otlp = EZ_OTLP(log=["logging", "structlog"])
```
##### Run template.py
```bash
docker-compose up
```
#### locally hosted SigNoz
> http://localhost:3301/
```bash
cd signoz
docker-compose up -d
```
### Images
![img.png](docs/img.png)
![img.png](docs/img2.png)
![img.png](docs/img3.png)
### Todolist
- logging module support -> loguru
- Integration with other services -> Litestar, FastAPI, Starlette, Aiohttp, sqlalchemy, redis
- Improve documentation

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# ez-otlp
> ez-otlp 是一个简单的工具,无需复杂的配置,即可便捷的使用现代化的监控系统,帮助开发者简化遥测数据的采集和传输,非常适用于现代云原生应用和微服务架构的服务中
> 通过 OpenTelemetry 高效收集如日志、指标、跟踪,可以与现有的监控工具和平台(如 Prometheus、Grafana、Datadog 等)无缝集成
🚀 快速实现 应用程序性能监控、日志管理、分布式跟踪、指标和仪表板、警报
🛠️ 通过环境变量解决99%的参数设置,开箱即用,无需繁琐的参数就可以与 OpenTelemetry 无缝集成。
❤️ demo 采用 SigNoz直观的界面体验现代化监控系统带来的拜尼马
### 使用说明
##### 项目中使用
```python
# env
# EZ_ENDPOINT = http://host.docker.internal:4317/v1/traces
# EZ_RESOURCE_SERVICE_NAME = ez-otlp
from ez_otlp import EZ_OTLP
otlp = EZ_OTLP(log=["logging", "structlog"])
```
##### 运行 template.py
```bash
docker-compose up
```
#### 启用本地托管的 SigNoz
> http://localhost:3301/
```bash
cd signoz
docker-compose up -d
```
### 图片
![img.png](docs/img.png)
![img.png](docs/img2.png)
![img.png](docs/img3.png)
### Todolist
- 日志模块支持 -> loguru
- 与其他服务集成 -> Litestar, FastAPI, Starlette, Aiohttp, sqlalchemy, redis
- 完善文档

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services:
app:
build:
context: .
dockerfile: Dockerfile
image: ez_otlp:latest
command: "python template.py"
environment:
EZ_RESOURCE_SERVICE_NAME: ez-otlp
EZ_ENDPOINT: http://host.docker.internal:4317/v1/traces
# EZ_ENDPOINT: http://host.docker.internal:4318/v1/traces
# EZ_gRPC: false
volumes:
- ./ez_otlp:/app/ez_otlp
- ./template.py:/app/template.py

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import logging
from typing import Union, Type, Optional, Dict, List
from grpc import ChannelCredentials
from opentelemetry import _logs
from opentelemetry import metrics
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc._log_exporter import (
OTLPLogExporter as gRPCLogExporter,
)
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import (
OTLPMetricExporter as gRPCMetricExporter,
)
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
OTLPSpanExporter as gRPCTraceExporter,
)
from opentelemetry.exporter.otlp.proto.http._log_exporter import (
OTLPLogExporter as HttpLogExporter,
)
from opentelemetry.exporter.otlp.proto.http.metric_exporter import (
OTLPMetricExporter as HttpMetricExporter,
)
from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
OTLPSpanExporter as HttpTraceExporter,
)
from opentelemetry.sdk._logs import LoggerProvider
from opentelemetry.sdk._logs._internal.export import BatchLogRecordProcessor
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics._internal.aggregation import Aggregation
from opentelemetry.sdk.metrics.export import AggregationTemporality
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import (
BatchSpanProcessor,
SimpleSpanProcessor,
)
from ez_otlp.open_telemetry.env_exporter import Exporter
from ez_otlp.open_telemetry.env_resource import EZResource
SpanProcessor = Union[
SimpleSpanProcessor,
BatchSpanProcessor,
]
class EZBase:
exporter: Exporter = None
resource: Resource = None
def __init__(
self,
exporter: Exporter = None,
resource: Resource | EZResource = None,
credentials: Optional[ChannelCredentials] = None,
):
if not self.exporter:
if not exporter:
exporter = Exporter(credentials=credentials)
self.exporter = exporter
if not self.resource:
if not resource:
resource = EZResource()
if isinstance(resource, EZResource):
resource: Resource = resource.get_resource()
self.resource = resource
class EZTracer(EZBase):
tracer_provider: TracerProvider
def __init__(
self,
exporter: Exporter = None,
resource: Resource | EZResource = None,
credentials: Optional[ChannelCredentials] = None,
/,
set_tracer_provider: bool = True,
span_processor_cls: Type[SpanProcessor] = BatchSpanProcessor,
span_exporter_cls: Union[gRPCTraceExporter | HttpTraceExporter] = None,
):
EZBase.__init__(self, exporter, resource, credentials)
self.tracer_provider = TracerProvider(resource=self.resource)
if set_tracer_provider:
trace.set_tracer_provider(self.tracer_provider)
self.tracer_provider.add_span_processor(
span_processor_cls(
span_exporter=self.exporter.get_span_exporter(
span_exporter_cls=span_exporter_cls
)
)
)
class EZMetric(EZBase):
meter_provider: MeterProvider
def __init__(
self,
exporter: Exporter = None,
resource: Resource | EZResource = None,
credentials: Optional[ChannelCredentials] = None,
/,
metric_exporter_cls: Union[gRPCMetricExporter, HttpMetricExporter] = None,
preferred_temporality: Dict[type, AggregationTemporality] = None,
preferred_aggregation: Dict[type, Aggregation] = None,
max_export_batch_size: Optional[int] = None,
set_meter_provider: bool = True,
):
EZBase.__init__(self, exporter, resource, credentials)
metric_exporter = self.exporter.get_metric_exporter(
metric_exporter_cls=metric_exporter_cls,
preferred_temporality=preferred_temporality,
preferred_aggregation=preferred_aggregation,
max_export_batch_size=max_export_batch_size,
)
self.meter_provider = MeterProvider(
resource=self.resource,
metric_readers=[
PeriodicExportingMetricReader(
exporter=metric_exporter,
),
],
views=[],
)
if set_meter_provider:
metrics.set_meter_provider(self.meter_provider)
class EZLog(EZBase):
logger_provider: LoggerProvider
def __init__(
self,
exporter: Exporter = None,
resource: Resource | EZResource = None,
credentials: Optional[ChannelCredentials] = None,
/,
log: str | List[str] = "logging",
log_lever: int = logging.DEBUG,
log_exporter_cls: gRPCLogExporter | HttpLogExporter = None,
set_logger_provider: bool = True,
):
EZBase.__init__(self, exporter, resource, credentials)
# 配置日志跟踪
self.logger_provider = LoggerProvider(resource=self.resource)
if set_logger_provider:
_logs.set_logger_provider(self.logger_provider)
self.logger_provider.add_log_record_processor(
BatchLogRecordProcessor(
exporter=self.exporter.get_log_export(log_exporter_cls=log_exporter_cls)
)
)
if isinstance(log, str):
log = [log]
if "logging" in log:
from opentelemetry.sdk._logs import LoggingHandler
logging.getLogger().addHandler(
LoggingHandler(level=log_lever, logger_provider=self.logger_provider)
)
if "structlog" in log:
from ez_otlp.log.structlog_config import get_struct_logging_config
config = get_struct_logging_config(
log_lever=log_lever, logger_provider=self.logger_provider
)
config.configure()
class EZ_OTLP(EZLog, EZMetric, EZTracer, EZBase):
def __init__(
self,
exporter: Exporter = None,
resource: Resource | EZResource = None,
credentials: Optional[ChannelCredentials] = None,
/,
set_tracer_provider: bool = True,
span_processor_cls: Type[SpanProcessor] = BatchSpanProcessor,
span_exporter_cls: Union[gRPCTraceExporter | HttpTraceExporter] = None,
metric_exporter_cls: Union[gRPCMetricExporter, HttpMetricExporter] = None,
preferred_temporality: Dict[type, AggregationTemporality] = None,
preferred_aggregation: Dict[type, Aggregation] = None,
max_export_batch_size: Optional[int] = None,
set_meter_provider: bool = True,
log: str | List[str] = "logging",
log_lever: int = logging.DEBUG,
log_exporter_cls: gRPCLogExporter | HttpLogExporter = None,
set_logger_provider: bool = True,
):
EZBase.__init__(self, exporter, resource, credentials)
EZTracer.__init__(
self,
set_tracer_provider=set_tracer_provider,
span_processor_cls=span_processor_cls,
span_exporter_cls=span_exporter_cls,
)
EZMetric.__init__(
self,
metric_exporter_cls=metric_exporter_cls,
preferred_temporality=preferred_temporality,
preferred_aggregation=preferred_aggregation,
max_export_batch_size=max_export_batch_size,
set_meter_provider=set_meter_provider,
)
EZLog.__init__(
self,
log=log,
log_lever=log_lever,
log_exporter_cls=log_exporter_cls,
set_logger_provider=set_logger_provider,
)

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"""https://www.structlog.org/"""
from dataclasses import dataclass, field
from typing import Callable
import logging
from datetime import datetime, timezone
import orjson
from opentelemetry._logs import std_to_otel
from opentelemetry.sdk._logs._internal import LoggerProvider, LogRecord
from opentelemetry.trace import get_current_span
import structlog
from structlog._log_levels import NAME_TO_LEVEL
from structlog.types import BindableLogger, Processor, WrappedLogger
@dataclass
class StructLoggingConfig:
processors: list[Processor] | None = field(default=None)
wrapper_class: type[BindableLogger] | None = field(default=None)
logger_factory: Callable[..., WrappedLogger] | None = field(default=None)
cache_logger_on_first_use: bool = field(default=True)
def configure(self):
structlog.configure(
processors=self.processors,
wrapper_class=self.wrapper_class,
logger_factory=self.logger_factory,
cache_logger_on_first_use=self.cache_logger_on_first_use,
)
return structlog.get_logger
_EXCLUDE_ATTRS = {"exception", "timestamp"}
_dumps = orjson.dumps
class StructlogHandler:
def __init__(self, logger_provider: LoggerProvider) -> None:
self._logger = logger_provider.get_logger(__name__)
def __call__(
self,
logger: structlog.typing.WrappedLogger,
name: str,
event_dict: structlog.typing.EventDict,
):
span_context = get_current_span().get_span_context()
severity_number = std_to_otel(NAME_TO_LEVEL[event_dict["level"]])
attributes = {
k: v if isinstance(v, (bool, str, bytes, int, float)) else _dumps(v)
for k, v in event_dict.items()
if k not in _EXCLUDE_ATTRS
}
self._logger.emit(
LogRecord(
timestamp=int(datetime.now(timezone.utc).timestamp() * 1e9),
trace_id=span_context.trace_id,
span_id=span_context.span_id,
trace_flags=span_context.trace_flags,
severity_text=event_dict["level"],
severity_number=severity_number,
body=event_dict["event"],
resource=self._logger.resource,
attributes=attributes,
)
)
return event_dict
def get_struct_logging_config(
logger_provider: LoggerProvider,
log_lever: int = logging.DEBUG,
) -> StructLoggingConfig:
return StructLoggingConfig(
processors=[
structlog.contextvars.merge_contextvars,
structlog.processors.add_log_level,
structlog.processors.format_exc_info,
structlog.processors.TimeStamper(fmt="iso", utc=True),
StructlogHandler(logger_provider),
structlog.processors.JSONRenderer(serializer=orjson.dumps),
],
logger_factory=structlog.BytesLoggerFactory(),
wrapper_class=structlog.make_filtering_bound_logger(log_lever),
cache_logger_on_first_use=True,
)

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from typing import Optional, Sequence, Union, Tuple, Dict, Literal
from grpc import ChannelCredentials, Compression
from opentelemetry.exporter.otlp.proto.grpc._log_exporter import (
OTLPLogExporter as gRPCLogExporter,
)
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import (
OTLPMetricExporter as gRPCMetricExporter,
)
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
OTLPSpanExporter as gRPCTraceExporter,
)
from opentelemetry.exporter.otlp.proto.http._log_exporter import (
OTLPLogExporter as HttpLogExporter,
)
from opentelemetry.exporter.otlp.proto.http.metric_exporter import (
OTLPMetricExporter as HttpMetricExporter,
)
from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
OTLPSpanExporter as HttpTraceExporter,
)
from opentelemetry.sdk._logs.export import LogExporter
from opentelemetry.sdk.metrics._internal.aggregation import Aggregation
from opentelemetry.sdk.metrics.export import AggregationTemporality
from opentelemetry.sdk.metrics.export import MetricExporter
from opentelemetry.sdk.trace.export import SpanExporter
from pydantic_settings import BaseSettings
class Exporter(BaseSettings):
endpoint: Optional[str] = None
gRPC: bool = True
# TODO<Martin>: credentials compatible env settings
credentials: Optional[ChannelCredentials] = None
headers: Optional[Union[Sequence[Tuple[str, str]], Dict[str, str], str]] = None
timeout: Optional[int] = None
compression: Literal[0, 1, 2] = 0 # 0 NoCompression 1 Deflate 2 Gzip
class Config:
env_prefix = "EZ_"
case_sensitive = False
def get_span_exporter(
self, span_exporter_cls: Union[gRPCTraceExporter | HttpTraceExporter] = None
) -> SpanExporter:
self.compression: Compression
if span_exporter_cls is None:
span_exporter_cls = gRPCTraceExporter if self.gRPC else HttpTraceExporter
return span_exporter_cls(
endpoint=self.endpoint,
headers=self.headers,
timeout=self.timeout,
compression=self.compression,
)
def get_log_export(
self, log_exporter_cls: gRPCLogExporter | HttpLogExporter = None
) -> LogExporter:
self.compression: Compression
if log_exporter_cls is None:
log_exporter_cls = gRPCLogExporter if self.gRPC else HttpLogExporter
return log_exporter_cls(
endpoint=self.endpoint,
headers=self.headers,
timeout=self.timeout,
compression=self.compression,
)
def get_metric_exporter(
self,
metric_exporter_cls: Union[gRPCMetricExporter, HttpMetricExporter] = None,
preferred_temporality: Dict[type, AggregationTemporality] = None,
preferred_aggregation: Dict[type, Aggregation] = None,
max_export_batch_size: Optional[int] = None,
) -> MetricExporter:
self.compression: Compression
if metric_exporter_cls is None:
metric_exporter_cls = (
gRPCMetricExporter if self.gRPC else HttpMetricExporter
)
return metric_exporter_cls(
endpoint=self.endpoint,
headers=self.headers,
timeout=self.timeout,
compression=self.compression,
preferred_temporality=preferred_temporality,
preferred_aggregation=preferred_aggregation,
)
if __name__ == "__main__":
print(Exporter(credentials=ChannelCredentials("test")).model_dump())

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import inspect
from opentelemetry.sdk.resources import Resource
from opentelemetry.semconv.resource import ResourceAttributes
from pydantic import create_model
from pydantic_settings import BaseSettings
ResourceAttributesModel = create_model(
"ResourceAttributesModel",
**{
name: (str | None, None)
for name, value in inspect.getmembers(
ResourceAttributes, predicate=lambda obj: isinstance(obj, str)
)
if name not in ["SCHEMA_URL", "__module__"]
},
)
class EZResource(ResourceAttributesModel, BaseSettings):
"""
Construct Resource using `env`
for example `SERVICE_NAME(service.name)`
EZ_RESOURCE_SERVICE_NAME=ez-otlp
"""
schema_url: str | None = None
class Config:
env_prefix = "EZ_RESOURCE_"
case_sensitive = False
def get_resource(self) -> Resource:
return Resource.create(
{
getattr(ResourceAttributes, k): v
for k, v in self.model_dump(exclude_none=True).items()
},
self.schema_url,
)
ez_resource = EZResource()
default_resource: Resource = ez_resource.get_resource()
if __name__ == "__main__":
print(ez_resource.model_dump(exclude_none=True))
print(default_resource.to_json())

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[project]
name = "ez-otlp"
version = "0.1.0"
description = "ez-otlp is a simple generation tool designed to help developers quickly generate OTLP data, supporting the efficient collection and transmission of telemetry data such as metrics, logs, and traces. This tool simplifies the process of constructing and sending OTLP data, making the integration of OpenTelemetry more convenient and faster. With an intuitive interface, developers can easily generate the required telemetry data and export it to monitoring systems without complex configurations. It is ideal for applications and systems that need to quickly build OTLP data streams."
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
"opentelemetry-api>=1.28.2",
"opentelemetry-exporter-otlp>=1.28.2",
"opentelemetry-instrumentation-logging>=0.49b2",
"opentelemetry-sdk>=1.28.2",
"orjson>=3.10.12",
"psutil>=6.1.0",
"pydantic-settings>=2.6.1",
"structlog>=24.4.0",
]

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# bitnami/zookeeper
IMG_ZOOKEEPER=3.7.1
# clickhouse/clickhouse-server
IMG_CLICKHOUSE=24.1.2-alpine
# signoz/alertmanager(告警)
IMG_ALERTMANAGER=0.23.7
# signoz/query-service(查询)
IMG_QUERY_SERVICE=0.60.0
# signoz/frontend(GUI)
IMG_FRONTEND=0.60.0
# signoz/locust(性能测试)
IMG_LOCUST=1.2.3
# signoz/signoz-otel-collector(otel 数据采集)
IMG_OTEL_COLLECTOR=0.111.13
# signoz/signoz-schema-migrator(数据库升级)
IMG_MIGRATOR=0.111.13

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<?xml version="1.0"?>
<clickhouse>
<!-- ZooKeeper is used to store metadata about replicas, when using Replicated tables.
Optional. If you don't use replicated tables, you could omit that.
See https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/replication/
-->
<zookeeper>
<node index="1">
<host>zookeeper</host>
<port>2181</port>
</node>
<!-- <node index="2">
<host>zookeeper-2</host>
<port>2181</port>
</node>
<node index="3">
<host>zookeeper-3</host>
<port>2181</port>
</node> -->
</zookeeper>
<!-- Configuration of clusters that could be used in Distributed tables.
https://clickhouse.com/docs/en/operations/table_engines/distributed/
-->
<remote_servers>
<cluster>
<!-- Inter-server per-cluster secret for Distributed queries
default: no secret (no authentication will be performed)
If set, then Distributed queries will be validated on shards, so at least:
- such cluster should exist on the shard,
- such cluster should have the same secret.
And also (and which is more important), the initial_user will
be used as current user for the query.
Right now the protocol is pretty simple and it only takes into account:
- cluster name
- query
Also it will be nice if the following will be implemented:
- source hostname (see interserver_http_host), but then it will depends from DNS,
it can use IP address instead, but then the you need to get correct on the initiator node.
- target hostname / ip address (same notes as for source hostname)
- time-based security tokens
-->
<!-- <secret></secret> -->
<shard>
<!-- Optional. Whether to write data to just one of the replicas. Default: false (write data to all replicas). -->
<!-- <internal_replication>false</internal_replication> -->
<!-- Optional. Shard weight when writing data. Default: 1. -->
<!-- <weight>1</weight> -->
<replica>
<host>clickhouse</host>
<port>9000</port>
<!-- Optional. Priority of the replica for load_balancing. Default: 1 (less value has more priority). -->
<!-- <priority>1</priority> -->
</replica>
</shard>
<!-- <shard>
<replica>
<host>clickhouse-2</host>
<port>9000</port>
</replica>
</shard>
<shard>
<replica>
<host>clickhouse-3</host>
<port>9000</port>
</replica>
</shard> -->
</cluster>
</remote_servers>
</clickhouse>

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<?xml version="1.0"?>
<clickhouse>
<!-- See also the files in users.d directory where the settings can be overridden. -->
<!-- Profiles of settings. -->
<profiles>
<!-- Default settings. -->
<default>
<!-- Maximum memory usage for processing single query, in bytes. -->
<max_memory_usage>10000000000</max_memory_usage>
<!-- How to choose between replicas during distributed query processing.
random - choose random replica from set of replicas with minimum number of errors
nearest_hostname - from set of replicas with minimum number of errors, choose replica
with minimum number of different symbols between replica's hostname and local hostname
(Hamming distance).
in_order - first live replica is chosen in specified order.
first_or_random - if first replica one has higher number of errors, pick a random one from replicas with minimum number of errors.
-->
<load_balancing>random</load_balancing>
</default>
<!-- Profile that allows only read queries. -->
<readonly>
<readonly>1</readonly>
</readonly>
</profiles>
<!-- Users and ACL. -->
<users>
<!-- If user name was not specified, 'default' user is used. -->
<default>
<!-- See also the files in users.d directory where the password can be overridden.
Password could be specified in plaintext or in SHA256 (in hex format).
If you want to specify password in plaintext (not recommended), place it in 'password' element.
Example: <password>qwerty</password>.
Password could be empty.
If you want to specify SHA256, place it in 'password_sha256_hex' element.
Example: <password_sha256_hex>65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5</password_sha256_hex>
Restrictions of SHA256: impossibility to connect to ClickHouse using MySQL JS client (as of July 2019).
If you want to specify double SHA1, place it in 'password_double_sha1_hex' element.
Example: <password_double_sha1_hex>e395796d6546b1b65db9d665cd43f0e858dd4303</password_double_sha1_hex>
If you want to specify a previously defined LDAP server (see 'ldap_servers' in the main config) for authentication,
place its name in 'server' element inside 'ldap' element.
Example: <ldap><server>my_ldap_server</server></ldap>
If you want to authenticate the user via Kerberos (assuming Kerberos is enabled, see 'kerberos' in the main config),
place 'kerberos' element instead of 'password' (and similar) elements.
The name part of the canonical principal name of the initiator must match the user name for authentication to succeed.
You can also place 'realm' element inside 'kerberos' element to further restrict authentication to only those requests
whose initiator's realm matches it.
Example: <kerberos />
Example: <kerberos><realm>EXAMPLE.COM</realm></kerberos>
How to generate decent password:
Execute: PASSWORD=$(base64 < /dev/urandom | head -c8); echo "$PASSWORD"; echo -n "$PASSWORD" | sha256sum | tr -d '-'
In first line will be password and in second - corresponding SHA256.
How to generate double SHA1:
Execute: PASSWORD=$(base64 < /dev/urandom | head -c8); echo "$PASSWORD"; echo -n "$PASSWORD" | sha1sum | tr -d '-' | xxd -r -p | sha1sum | tr -d '-'
In first line will be password and in second - corresponding double SHA1.
-->
<password></password>
<!-- List of networks with open access.
To open access from everywhere, specify:
<ip>::/0</ip>
To open access only from localhost, specify:
<ip>::1</ip>
<ip>127.0.0.1</ip>
Each element of list has one of the following forms:
<ip> IP-address or network mask. Examples: 213.180.204.3 or 10.0.0.1/8 or 10.0.0.1/255.255.255.0
2a02:6b8::3 or 2a02:6b8::3/64 or 2a02:6b8::3/ffff:ffff:ffff:ffff::.
<host> Hostname. Example: server01.clickhouse.com.
To check access, DNS query is performed, and all received addresses compared to peer address.
<host_regexp> Regular expression for host names. Example, ^server\d\d-\d\d-\d\.clickhouse\.com$
To check access, DNS PTR query is performed for peer address and then regexp is applied.
Then, for result of PTR query, another DNS query is performed and all received addresses compared to peer address.
Strongly recommended that regexp is ends with $
All results of DNS requests are cached till server restart.
-->
<networks>
<ip>::/0</ip>
</networks>
<!-- Settings profile for user. -->
<profile>default</profile>
<!-- Quota for user. -->
<quota>default</quota>
<!-- User can create other users and grant rights to them. -->
<!-- <access_management>1</access_management> -->
</default>
</users>
<!-- Quotas. -->
<quotas>
<!-- Name of quota. -->
<default>
<!-- Limits for time interval. You could specify many intervals with different limits. -->
<interval>
<!-- Length of interval. -->
<duration>3600</duration>
<!-- No limits. Just calculate resource usage for time interval. -->
<queries>0</queries>
<errors>0</errors>
<result_rows>0</result_rows>
<read_rows>0</read_rows>
<execution_time>0</execution_time>
</interval>
</default>
</quotas>
</clickhouse>

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<functions>
<function>
<type>executable</type>
<name>histogramQuantile</name>
<return_type>Float64</return_type>
<argument>
<type>Array(Float64)</type>
<name>buckets</name>
</argument>
<argument>
<type>Array(Float64)</type>
<name>counts</name>
</argument>
<argument>
<type>Float64</type>
<name>quantile</name>
</argument>
<format>CSV</format>
<command>./histogramQuantile</command>
</function>
</functions>

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<clickhouse>
<logger>
<!-- Possible levels [1]:
- none (turns off logging)
- fatal
- critical
- error
- warning
- notice
- information
- debug
- trace
[1]: https://github.com/pocoproject/poco/blob/poco-1.9.4-release/Foundation/include/Poco/Logger.h#L105-L114
-->
<level>information</level>
<log>/var/log/clickhouse-keeper/clickhouse-keeper.log</log>
<errorlog>/var/log/clickhouse-keeper/clickhouse-keeper.err.log</errorlog>
<!-- Rotation policy
See https://github.com/pocoproject/poco/blob/poco-1.9.4-release/Foundation/include/Poco/FileChannel.h#L54-L85
-->
<size>1000M</size>
<count>10</count>
<!-- <console>1</console> --> <!-- Default behavior is autodetection (log to console if not daemon mode and is tty) -->
</logger>
<listen_host>0.0.0.0</listen_host>
<max_connections>4096</max_connections>
<keeper_server>
<tcp_port>9181</tcp_port>
<!-- Must be unique among all keeper serves -->
<server_id>1</server_id>
<log_storage_path>/var/lib/clickhouse/coordination/logs</log_storage_path>
<snapshot_storage_path>/var/lib/clickhouse/coordination/snapshots</snapshot_storage_path>
<coordination_settings>
<operation_timeout_ms>10000</operation_timeout_ms>
<min_session_timeout_ms>10000</min_session_timeout_ms>
<session_timeout_ms>100000</session_timeout_ms>
<raft_logs_level>information</raft_logs_level>
<compress_logs>false</compress_logs>
<!-- All settings listed in https://github.com/ClickHouse/ClickHouse/blob/master/src/Coordination/CoordinationSettings.h -->
</coordination_settings>
<!-- enable sanity hostname checks for cluster configuration (e.g. if localhost is used with remote endpoints) -->
<hostname_checks_enabled>true</hostname_checks_enabled>
<raft_configuration>
<server>
<id>1</id>
<!-- Internal port and hostname -->
<hostname>clickhouses-keeper-1</hostname>
<port>9234</port>
</server>
<!-- Add more servers here -->
</raft_configuration>
</keeper_server>
</clickhouse>

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receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318
hostmetrics:
collection_interval: 30s
scrapers:
cpu: {}
load: {}
memory: {}
disk: {}
filesystem: {}
network: {}
processors:
batch:
send_batch_size: 10000
send_batch_max_size: 11000
timeout: 10s
# memory_limiter:
# # 80% of maximum memory up to 2G
# limit_mib: 1500
# # 25% of limit up to 2G
# spike_limit_mib: 512
# check_interval: 5s
#
# # 50% of the maximum memory
# limit_percentage: 50
# # 20% of max memory usage spike expected
# spike_limit_percentage: 20
# queued_retry:
# num_workers: 4
# queue_size: 100
# retry_on_failure: true
resourcedetection:
# Using OTEL_RESOURCE_ATTRIBUTES envvar, env detector adds custom labels.
detectors: [env, system] # include ec2 for AWS, gcp for GCP and azure for Azure.
timeout: 2s
signozspanmetrics/delta:
metrics_exporter: clickhousemetricswrite
metrics_flush_interval: 60s
latency_histogram_buckets: [100us, 1ms, 2ms, 6ms, 10ms, 50ms, 100ms, 250ms, 500ms, 1000ms, 1400ms, 2000ms, 5s, 10s, 20s, 40s, 60s ]
dimensions_cache_size: 100000
aggregation_temporality: AGGREGATION_TEMPORALITY_DELTA
enable_exp_histogram: true
dimensions:
- name: service.namespace
default: default
- name: deployment.environment
default: default
# This is added to ensure the uniqueness of the timeseries
# Otherwise, identical timeseries produced by multiple replicas of
# collectors result in incorrect APM metrics
- name: signoz.collector.id
- name: service.version
- name: browser.platform
- name: browser.mobile
- name: k8s.cluster.name
- name: k8s.node.name
- name: k8s.namespace.name
- name: host.name
- name: host.type
- name: container.name
extensions:
health_check:
endpoint: 0.0.0.0:13133
zpages:
endpoint: 0.0.0.0:55679
pprof:
endpoint: 0.0.0.0:1777
exporters:
clickhousetraces:
datasource: tcp://clickhouse:9000/signoz_traces
low_cardinal_exception_grouping: ${env:LOW_CARDINAL_EXCEPTION_GROUPING}
clickhousemetricswrite:
endpoint: tcp://clickhouse:9000/signoz_metrics
resource_to_telemetry_conversion:
enabled: true
clickhousemetricswrite/prometheus:
endpoint: tcp://clickhouse:9000/signoz_metrics
clickhousemetricswritev2:
dsn: tcp://clickhouse:9000/signoz_metrics
clickhouselogsexporter:
dsn: tcp://clickhouse:9000/signoz_logs
timeout: 10s
use_new_schema: true
# logging: {}
service:
telemetry:
logs:
encoding: json
metrics:
address: 0.0.0.0:8888
extensions:
- health_check
- zpages
- pprof
pipelines:
traces:
receivers: [otlp]
processors: [signozspanmetrics/delta, batch]
exporters: [clickhousetraces]
metrics:
receivers: [otlp]
processors: [batch]
exporters: [clickhousemetricswrite, clickhousemetricswritev2]
logs:
receivers: [otlp]
processors: [batch]
exporters: [clickhouselogsexporter]

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server_endpoint: ws://query-service:4320/v1/opamp

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# my global config
global:
scrape_interval: 5s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
# scrape_timeout is set to the global default (10s).
# Alertmanager configuration
alerting:
alertmanagers:
- static_configs:
- targets:
- alertmanager:9093
# Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
rule_files:
# - "first_rules.yml"
# - "second_rules.yml"
- 'alerts.yml'
# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs: []
remote_read:
- url: tcp://clickhouse:9000/signoz_metrics

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package main
import (
"bufio"
"fmt"
"math"
"os"
"sort"
"strconv"
"strings"
)
// NOTE: executable must be built with target OS and architecture set to linux/amd64
// env GOOS=linux GOARCH=amd64 go build -o histogramQuantile histogramQuantile.go
// The following code is adapted from the following source:
// https://github.com/prometheus/prometheus/blob/main/promql/quantile.go
type bucket struct {
upperBound float64
count float64
}
// buckets implements sort.Interface.
type buckets []bucket
func (b buckets) Len() int { return len(b) }
func (b buckets) Swap(i, j int) { b[i], b[j] = b[j], b[i] }
func (b buckets) Less(i, j int) bool { return b[i].upperBound < b[j].upperBound }
// bucketQuantile calculates the quantile 'q' based on the given buckets. The
// buckets will be sorted by upperBound by this function (i.e. no sorting
// needed before calling this function). The quantile value is interpolated
// assuming a linear distribution within a bucket. However, if the quantile
// falls into the highest bucket, the upper bound of the 2nd highest bucket is
// returned. A natural lower bound of 0 is assumed if the upper bound of the
// lowest bucket is greater 0. In that case, interpolation in the lowest bucket
// happens linearly between 0 and the upper bound of the lowest bucket.
// However, if the lowest bucket has an upper bound less or equal 0, this upper
// bound is returned if the quantile falls into the lowest bucket.
//
// There are a number of special cases (once we have a way to report errors
// happening during evaluations of AST functions, we should report those
// explicitly):
//
// If 'buckets' has 0 observations, NaN is returned.
//
// If 'buckets' has fewer than 2 elements, NaN is returned.
//
// If the highest bucket is not +Inf, NaN is returned.
//
// If q==NaN, NaN is returned.
//
// If q<0, -Inf is returned.
//
// If q>1, +Inf is returned.
func bucketQuantile(q float64, buckets buckets) float64 {
if math.IsNaN(q) {
return math.NaN()
}
if q < 0 {
return math.Inf(-1)
}
if q > 1 {
return math.Inf(+1)
}
sort.Sort(buckets)
if !math.IsInf(buckets[len(buckets)-1].upperBound, +1) {
return math.NaN()
}
buckets = coalesceBuckets(buckets)
ensureMonotonic(buckets)
if len(buckets) < 2 {
return math.NaN()
}
observations := buckets[len(buckets)-1].count
if observations == 0 {
return math.NaN()
}
rank := q * observations
b := sort.Search(len(buckets)-1, func(i int) bool { return buckets[i].count >= rank })
if b == len(buckets)-1 {
return buckets[len(buckets)-2].upperBound
}
if b == 0 && buckets[0].upperBound <= 0 {
return buckets[0].upperBound
}
var (
bucketStart float64
bucketEnd = buckets[b].upperBound
count = buckets[b].count
)
if b > 0 {
bucketStart = buckets[b-1].upperBound
count -= buckets[b-1].count
rank -= buckets[b-1].count
}
return bucketStart + (bucketEnd-bucketStart)*(rank/count)
}
// coalesceBuckets merges buckets with the same upper bound.
//
// The input buckets must be sorted.
func coalesceBuckets(buckets buckets) buckets {
last := buckets[0]
i := 0
for _, b := range buckets[1:] {
if b.upperBound == last.upperBound {
last.count += b.count
} else {
buckets[i] = last
last = b
i++
}
}
buckets[i] = last
return buckets[:i+1]
}
// The assumption that bucket counts increase monotonically with increasing
// upperBound may be violated during:
//
// * Recording rule evaluation of histogram_quantile, especially when rate()
// has been applied to the underlying bucket timeseries.
// * Evaluation of histogram_quantile computed over federated bucket
// timeseries, especially when rate() has been applied.
//
// This is because scraped data is not made available to rule evaluation or
// federation atomically, so some buckets are computed with data from the
// most recent scrapes, but the other buckets are missing data from the most
// recent scrape.
//
// Monotonicity is usually guaranteed because if a bucket with upper bound
// u1 has count c1, then any bucket with a higher upper bound u > u1 must
// have counted all c1 observations and perhaps more, so that c >= c1.
//
// Randomly interspersed partial sampling breaks that guarantee, and rate()
// exacerbates it. Specifically, suppose bucket le=1000 has a count of 10 from
// 4 samples but the bucket with le=2000 has a count of 7 from 3 samples. The
// monotonicity is broken. It is exacerbated by rate() because under normal
// operation, cumulative counting of buckets will cause the bucket counts to
// diverge such that small differences from missing samples are not a problem.
// rate() removes this divergence.)
//
// bucketQuantile depends on that monotonicity to do a binary search for the
// bucket with the φ-quantile count, so breaking the monotonicity
// guarantee causes bucketQuantile() to return undefined (nonsense) results.
//
// As a somewhat hacky solution until ingestion is atomic per scrape, we
// calculate the "envelope" of the histogram buckets, essentially removing
// any decreases in the count between successive buckets.
func ensureMonotonic(buckets buckets) {
max := buckets[0].count
for i := 1; i < len(buckets); i++ {
switch {
case buckets[i].count > max:
max = buckets[i].count
case buckets[i].count < max:
buckets[i].count = max
}
}
}
// End of copied code.
func readLines() []string {
r := bufio.NewReader(os.Stdin)
bytes := []byte{}
lines := []string{}
for {
line, isPrefix, err := r.ReadLine()
if err != nil {
break
}
bytes = append(bytes, line...)
if !isPrefix {
str := strings.TrimSpace(string(bytes))
if len(str) > 0 {
lines = append(lines, str)
bytes = []byte{}
}
}
}
if len(bytes) > 0 {
lines = append(lines, string(bytes))
}
return lines
}
func main() {
lines := readLines()
for _, text := range lines {
// Example input
// "[1, 2, 4, 8, 16]", "[1, 5, 8, 10, 14]", 0.9"
// bounds - counts - quantile
parts := strings.Split(text, "\",")
var bucketNumbers []float64
// Strip the ends with square brackets
text = parts[0][2 : len(parts[0])-1]
// Parse the bucket bounds
for _, num := range strings.Split(text, ",") {
num = strings.TrimSpace(num)
number, err := strconv.ParseFloat(num, 64)
if err == nil {
bucketNumbers = append(bucketNumbers, number)
}
}
var bucketCounts []float64
// Strip the ends with square brackets
text = parts[1][2 : len(parts[1])-1]
// Parse the bucket counts
for _, num := range strings.Split(text, ",") {
num = strings.TrimSpace(num)
number, err := strconv.ParseFloat(num, 64)
if err == nil {
bucketCounts = append(bucketCounts, number)
}
}
// Parse the quantile
q, err := strconv.ParseFloat(parts[2], 64)
var b buckets
if err == nil {
for i := 0; i < len(bucketNumbers); i++ {
b = append(b, bucket{upperBound: bucketNumbers[i], count: bucketCounts[i]})
}
}
fmt.Println(bucketQuantile(q, b))
}
}

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map $http_upgrade $connection_upgrade {
default upgrade;
'' close;
}
server {
listen 3301;
server_name _;
gzip on;
gzip_static on;
gzip_types text/plain text/css application/json application/x-javascript text/xml application/xml application/xml+rss text/javascript;
gzip_proxied any;
gzip_vary on;
gzip_comp_level 6;
gzip_buffers 16 8k;
gzip_http_version 1.1;
# to handle uri issue 414 from nginx
client_max_body_size 24M;
large_client_header_buffers 8 128k;
location / {
if ( $uri = '/index.html' ) {
add_header Cache-Control no-store always;
}
root /usr/share/nginx/html;
index index.html index.htm;
try_files $uri $uri/ /index.html;
}
location ~ ^/api/(v1|v3)/logs/(tail|livetail){
proxy_pass http://query-service:8080;
proxy_http_version 1.1;
# connection will be closed if no data is read for 600s between successive read operations
proxy_read_timeout 600s;
# dont buffer the data send it directly to client.
proxy_buffering off;
proxy_cache off;
}
location /api {
proxy_pass http://query-service:8080/api;
# connection will be closed if no data is read for 600s between successive read operations
proxy_read_timeout 600s;
}
location /ws {
proxy_pass http://query-service:8080/ws;
proxy_http_version 1.1;
proxy_set_header Upgrade "websocket";
proxy_set_header Connection "upgrade";
proxy_read_timeout 86400;
}
# redirect server error pages to the static page /50x.html
#
error_page 500 502 503 504 /50x.html;
location = /50x.html {
root /usr/share/nginx/html;
}
}

174
signoz/docker-compose.yaml Normal file
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services:
zookeeper:
image: bitnami/zookeeper:${IMG_ZOOKEEPER}
container_name: signoz-zookeeper
hostname: zookeeper
user: root
# ports:
# - "2181:2181"
# - "2888:2888"
# - "3888:3888"
# volumes:
# - ./data/zookeeper:/bitnami/zookeeper
environment:
- ZOO_SERVER_ID=1
- ALLOW_ANONYMOUS_LOGIN=yes
- ZOO_AUTOPURGE_INTERVAL=1
clickhouse:
image: clickhouse/clickhouse-server:${IMG_CLICKHOUSE}
tty: true
container_name: signoz-clickhouse
hostname: clickhouse
ports:
# - "9000:9000"
- "8123:8123"
# - "9181:9181"
volumes:
- ./clickhouse-setup/clickhouse-config.xml:/etc/clickhouse-server/config.xml
- ./clickhouse-setup/clickhouse-users.xml:/etc/clickhouse-server/users.xml
- ./clickhouse-setup/custom-function.xml:/etc/clickhouse-server/custom-function.xml
- ./clickhouse-setup/clickhouse-cluster.xml:/etc/clickhouse-server/config.d/cluster.xml
# - ./data/clickhouse/:/var/lib/clickhouse/
- ./clickhouse-setup/user_scripts:/var/lib/clickhouse/user_scripts/
healthcheck:
test:
[
"CMD",
"wget",
"--spider",
"-q",
"0.0.0.0:8123/ping"
]
interval: 30s
timeout: 5s
retries: 3
alertmanager:
image: signoz/alertmanager:${IMG_ALERTMANAGER}
container_name: signoz-alertmanager
# volumes:
# - ./data/alertmaRnager:/data
depends_on:
query-service:
condition: service_healthy
command:
- --queryService.url=http://query-service:8085
- --storage.path=/data
query-service:
image: signoz/query-service:${IMG_QUERY_SERVICE}
container_name: signoz-query-service
command:
[
"-config=/root/config/prometheus.yml",
"--use-logs-new-schema=true"
]
# ports:
# - "6060:6060" # pprof port
# - "8080:8080" # query-service port
volumes:
- ./clickhouse-setup/prometheus.yml:/root/config/prometheus.yml
- ./signoz_data/:/var/lib/signoz/
environment:
- ClickHouseUrl=tcp://clickhouse:9000
- ALERTMANAGER_API_PREFIX=http://alertmanager:9093/api/
- SIGNOZ_LOCAL_DB_PATH=/var/lib/signoz/signoz.db
- DASHBOARDS_PATH=/root/config/dashboards
- STORAGE=clickhouse
- GODEBUG=netdns=go
- TELEMETRY_ENABLED=true
- DEPLOYMENT_TYPE=docker-standalone-amd
healthcheck:
test:
[
"CMD",
"wget",
"--spider",
"-q",
"localhost:8080/api/v1/health"
]
interval: 30s
timeout: 5s
retries: 3
depends_on:
clickhouse:
condition: service_healthy
otel-collector-migrator-sync:
condition: service_completed_successfully
frontend:
image: signoz/frontend:${IMG_FRONTEND}
container_name: signoz-frontend
depends_on:
- alertmanager
- query-service
ports:
- "3301:3301"
volumes:
- ./common/nginx-config.conf:/etc/nginx/conf.d/default.conf
otel-collector:
image: signoz/signoz-otel-collector:${IMG_OTEL_COLLECTOR}
container_name: signoz-otel-collector
command:
[
"--config=/etc/otel-collector-config.yaml",
"--manager-config=/etc/manager-config.yaml",
"--copy-path=/var/tmp/collector-config.yaml",
"--feature-gates=-pkg.translator.prometheus.NormalizeName"
]
user: root
volumes:
- ./clickhouse-setup/otel-collector-config.yaml:/etc/otel-collector-config.yaml
- ./clickhouse-setup/otel-collector-opamp-config.yaml:/etc/manager-config.yaml
# - /:/hostfs:ro
environment:
- OTEL_RESOURCE_ATTRIBUTES=host.name=signoz-host,os.type=linux
- LOW_CARDINAL_EXCEPTION_GROUPING=false
ports:
# - "1777:1777" # pprof extension
- "4317:4317" # OTLP gRPC receiver
- "4318:4318" # OTLP HTTP receiver
# - "8888:8888" # OtelCollector internal metrics
# - "8889:8889" # signoz spanmetrics exposed by the agent
# - "9411:9411" # Zipkin port
# - "13133:13133" # health check extension
# - "14250:14250" # Jaeger gRPC
# - "14268:14268" # Jaeger thrift HTTP
# - "55678:55678" # OpenCensus receiver
# - "55679:55679" # zPages extension
depends_on:
clickhouse:
condition: service_healthy
otel-collector-migrator-sync:
condition: service_completed_successfully
query-service:
condition: service_healthy
# 数据库初始化
otel-collector-migrator-sync:
image: signoz/signoz-schema-migrator:${IMG_MIGRATOR}
container_name: signoz-otel-migrator-sync
command:
- "sync"
- "--dsn=tcp://clickhouse:9000"
- "--up="
depends_on:
clickhouse:
condition: service_healthy
otel-collector-migrator-async:
image: signoz/signoz-schema-migrator:${IMG_MIGRATOR}
container_name: signoz-otel-migrator-async
command:
- "async"
- "--dsn=tcp://clickhouse:9000"
- "--up="
depends_on:
clickhouse:
condition: service_healthy
otel-collector-migrator-sync:
condition: service_completed_successfully

23
template.py Normal file
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@ -0,0 +1,23 @@
if __name__ == "__main__":
import time
from opentelemetry import trace
import logging
import structlog
from ez_otlp import EZ_OTLP
otlp = EZ_OTLP(log=["logging", "structlog"])
print(otlp.exporter.model_dump())
print(otlp.resource.to_json())
logging.getLogger().addHandler(logging.StreamHandler())
logging_logger = logging.getLogger("otlp.logging")
structlog_logger = structlog.get_logger("otlp.structlog")
tracer = trace.get_tracer(__name__)
for index in range(60):
with tracer.start_as_current_span(f"tracer {index}"):
logging_logger.error("Hello from OpenTelemetry! (logging)")
structlog_logger.error("Hello from OpenTelemetry! (structlog)", index=index)
time.sleep(0.5)

528
uv.lock generated Normal file
View File

@ -0,0 +1,528 @@
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