streams-explorer 2.3.2

Explore Data Pipelines in Apache Kafka.

Streams Explorer

Explore Apache Kafka data pipelines in Kubernetes.

Note We are participating in the annual Hacktoberfest. If you're looking to contribute, please see our open issues and use the standalone installation for development.

Contents

• Streams Explorer
  • Features
  • Overview
  • Installation
    • Docker Compose
    • Deploying to Kubernetes cluster
    • Standalone
      • Backend
      • Frontend
  • Configuration
    • Kafka
    • Kafka Connect
    • Kubernetes
    • Schema Registry / Karapace
    • Prometheus
    • AKHQ
    • Redpanda Console
    • Grafana
    • Kibana
    • Elasticsearch
    • Plugins
  • Demo pipeline
  • Plugin customization

Features

• Visualization of streaming applications, topics, and connectors
• Monitor all or individual pipelines from multiple namespaces
• Inspection of Avro schema from schema registry
• Integration with streams-bootstrap and faust-bootstrap, or custom streaming app config parsing from Kubernetes deployments using plugins
• Real-time metrics from Prometheus (consumer lag & read rate, replicas, topic size, messages in & out per second, connector tasks)
• Linking to external services for logging and analysis, such as Kibana, Grafana, Loki, AKHQ, Redpanda Console, and Elasticsearch
• Customizable through Python plugins

Overview

Visit our introduction blogpost for a complete overview and demo of Streams Explorer.

Installation

Prerequisites Access to a Kubernetes cluster, where streaming apps and services are deployed.

Docker Compose

1. Forward the ports to Prometheus. (Kafka Connect, Schema Registry, and other integrations are optional)
2. Start the container

docker compose up

Once the container is started visit http://localhost:8080

Deploying to Kubernetes cluster

1. Add the Helm chart repository

helm repo add streams-explorer https://bakdata.github.io/streams-explorer

2. Install

helm upgrade --install --values helm-chart/values.yaml streams-explorer streams-explorer/streams-explorer

Standalone

Backend

1. Install dependencies using Poetry

poetry install

2. Forward the ports to Prometheus. (Kafka Connect, Schema Registry, and other integrations are optional)
3. Configure the backend in settings.yaml.
4. Start the backend server

poetry run start

Frontend

1. Install dependencies

npm ci

2. Start the frontend server

npm run build && npm run prod

Visit http://localhost:3000

Configuration

Depending on your type of installation set the configuration for the backend server in this file:

• Docker Compose: docker-compose.yaml
• Kubernetes: helm-chart/values.yaml
• standalone: backend/settings.yaml

In the helm-chart/values.yaml configuration is done either through the config section using double underscore notation, e.g. K8S__consumer_group_annotation: consumerGroup or the content of backend/settings.yaml can be pasted under the settings section. Alternatively all configuration options can be written as environment variables using double underscore notation and the prefix SE, e.g. SE_K8S__deployment__cluster=false.

The following configuration options are available:

General

• graph.update_interval Render the graph every x seconds (int, required, default: 30)
• graph.layout_arguments Arguments passed to graphviz layout (string, required, default: -Grankdir=LR -Gnodesep=0.8 -Gpad=10)
• graph.pipeline_distance Increase/decrease vertical space between pipeline graphs by X pixels (int, required, default: 500)
• graph.resolve.input_pattern_topics.all If true topics that match (extra) input pattern(s) are connected to the streaming app in the graph containing all pipelines (bool, required, default: false)
• graph.resolve.input_pattern_topics.pipelines If true topics that match (extra) input pattern(s) are connected to the streaming app in pipeline graphs (bool, required, default: false)

Kafka

• kafka.enable Enable Kafka (bool, default: false)
• kafka.config librdkafka configuration properties (reference) (dict, default: {"bootstrap.servers": "localhost:9092"})
• kafka.displayed_information Configuration options of Kafka topics displayed in the frontend (list of dict)
• kafka.topic_names_cache.ttl Cache for retrieving all topic names (used when input topic patterns are resolved) (int, default: 3600)

Kafka Connect

• kafkaconnect.url URL of Kafka Connect server (string, default: None)
• kafkaconnect.update_interval Fetch connectors every x seconds (int, default: 300)
• kafkaconnect.displayed_information Configuration options of Kafka connectors displayed in the frontend (list of dict)

Kubernetes

• k8s.deployment.cluster Whether streams-explorer is deployed to Kubernetes cluster (bool, required, default: false)
• k8s.deployment.context Name of cluster (string, optional if running in cluster, default: kubernetes-cluster)
• k8s.deployment.namespaces Kubernetes namespaces (list of string, required, default: ['kubernetes-namespace'])
• k8s.containers.ignore Name of containers that should be ignored/hidden (list of string, default: ['prometheus-jmx-exporter'])
• k8s.displayed_information Details of pod that should be displayed (list of dict, default: [{'name': 'Labels', 'key': 'metadata.labels'}])
• k8s.labels Labels used to set attributes of nodes (list of string, required, default: ['pipeline'])
• k8s.pipeline.label Attribute of nodes the pipeline name should be extracted from (string, required, default: pipeline)
• k8s.consumer_group_annotation Annotation the consumer group name should be extracted from (string, required, default: consumerGroup)

Schema Registry / Karapace

• schemaregistry.url URL of Confluent Schema Registry or Karapace (string, default: None)

Prometheus

• prometheus.url URL of Prometheus (string, required, default: http://localhost:9090)

The following exporters are required to collect Kafka metrics for Prometheus:

• Kafka Exporter
• Kafka Lag Exporter
• Kafka Connect Exporter

AKHQ

• akhq.enable Enable AKHQ (bool, default: false)
• akhq.url URL of AKHQ (string, default: http://localhost:8080)
• akhq.cluster Name of cluster (string, default: kubernetes-cluster)
• akhq.connect Name of connect (string, default: None)

Redpanda Console

Redpanda Console can be used instead of AKHQ. (mutually exclusive)

• redpanda_console.enable Enable Redpanda Console (bool, default: false)
• redpanda_console.url URL of Redpanda Console (string, default: http://localhost:8080)

Grafana

• grafana.enable Enable Grafana (bool, default: false)
• grafana.url URL of Grafana (string, default: http://localhost:3000)
• grafana.dashboards.topics Path to topics dashboard (string), sample dashboards for topics and consumer groups are included in the ./grafana subfolder
• grafana.dashboards.consumergroups Path to consumer groups dashboard (string)

Kibana

• kibanalogs.enable Enable Kibana logs (bool, default: false)
• kibanalogs.url URL of Kibana logs (string, default: http://localhost:5601)

Loki

Loki can be used instead of Kibana. (mutually exclusive)

• loki.enable Enable Loki logs (bool, default: false)
• loki.url URL of Loki logs (string, default: http://localhost:3000)

Elasticsearch

for Kafka Connect Elasticsearch connector

• esindex.url URL of Elasticsearch index (string, default: http://localhost:5601/app/kibana#/dev_tools/console)

Plugins

• plugins.path Path to folder containing plugins relative to backend (string, required, default: ./plugins)
• plugins.extractors.default Whether to load default extractors (bool, required, default: true)

Demo pipeline

ATM Fraud detection with streams-bootstrap

Plugin customization

It is possible to create your own config parser, linker, metric provider, and extractors in Python by implementing the K8sConfigParser, LinkingService, MetricProvider, or Extractor classes. This way you can customize it to your specific setup and services. As an example we provide the DefaultLinker as LinkingService. The default MetricProvider supports Prometheus. Furthermore the following default Extractor plugins are included:

• ElasticsearchSink
• JdbcSink
• S3Sink
• GenericSink/GenericSource

If your streaming application deployments are configured through environment variables, following the schema of streams-bootstrap or faust-bootstrap, the Streams Explorer works out-of-the-box with the default deployment parser. For streams-bootstrap deployments configured through CLI arguments a separate parser can be loaded by creating a Python file (e.g. config_parser.py) in the plugins folder with the following import statement:

from streams_explorer.core.k8s_config_parser import StreamsBootstrapArgsParser

For other setups a custom config parser plugin can be created by inheriting from the K8sConfigParser class and implementing the parse method. In this example we're retrieving the streaming app configurations from an external REST API. In order for a deployment to be indentified as streaming app, input and output topics are required.

import httpx

from streams_explorer.core.k8s_config_parser import K8sConfigParser
from streams_explorer.models.k8s import K8sConfig


class CustomConfigParser(K8sConfigParser):
    def get_name(self) -> str:
        name = self.k8s_app.metadata.name
        if not name:
            raise TypeError(f"Name is required for {self.k8s_app.class_name}")
        return name

    def parse(self) -> K8sConfig:
        """Retrieve app config from REST endpoint."""
        name = self.get_name()
        data = httpx.get(f"url/config/{name}").json()
        return K8sConfig(**data)