Apache Flume
Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log and event data. It provides a simple and flexible architecture based on streaming data flows with pluggable sources, channels, and sinks, plus a REST monitoring API for agent metrics.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Apache Flume the way a machine reads it — 37 machine-readable artifacts across 2 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Apache Flume scores 50.5/100 (developing), with a separate agent-readiness read of 39/100 (agent aware). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
How we profile Apache Flume
Each block below is one kind of artifact we hold for Apache Flume. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 2
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Apache Flume Java API
Java API for building custom Flume sources, channels, sinks, and interceptors. Provides interfaces for developing pluggable data ingestion components.
Apache Flume Monitoring API
The Monitoring API from Apache Flume — 1 operation(s) for monitoring.
Open Collections 1
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Apache Flume Monitoring API
OPEN COLLECTIONPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Apache Flume Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Apache Flume Finops
FINOPSFeatures 9
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Pluggable Sources
Extensible source architecture supporting Avro, Thrift, Exec, Taildir, Kafka, HTTP, Syslog, and custom sources.
Durable Channels
Multiple channel implementations including memory, file-backed, and Kafka-backed channels for different durability requirements.
Multi-Destination Sinks
Write events to HDFS, HBase, Solr, Elasticsearch, Kafka, and custom sink destinations.
Fan-In Consolidation
Aggregate events from multiple agent sources into a single destination for centralized log collection.
Fan-Out Distribution
Route events from a single source to multiple channel/sink combinations for parallel processing.
Interceptors
Event transformation interceptors for filtering, enrichment, and routing based on event content.
SSL/TLS Security
TLS encryption support across Avro, Thrift, Kafka, HTTP, and Syslog components.
Monitoring REST API
HTTP monitoring endpoint exposing source, channel, and sink metrics for agent health monitoring.
Multi-Hop Flows
Chain multiple Flume agents via Avro/Thrift RPC for tiered log aggregation architectures.
Scroll within the panel for all 9 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Spectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Apache Flume API Rules
SPECTRALApache Flume API Rules
SPECTRALJSON Schema 2
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
AgentMetrics
JSON SCHEMAComponentMetrics
JSON SCHEMAJSON Structure 2
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Flume Monitoring Agent Metrics Structure
JSON STRUCTUREFlume Monitoring Component Metrics Structure
JSON STRUCTUREExamples 2
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Centralized Log Aggregation
Collect application logs from hundreds of servers and aggregate them into HDFS, Kafka, or Elasticsearch.
Real-Time Log Tailing
Tail application log files in real time using Taildir source for immediate event processing.
Syslog Ingestion
Ingest RFC-3164 and RFC-5424 syslog events from network devices into centralized storage.
Kafka Event Ingestion
Bridge Kafka topics to HDFS or other storage for batch analytics on streaming event data.
Multi-Tier Architectures
Build tiered data collection with edge collectors forwarding to aggregation agents and final destinations.
Integrations 5
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Apache Kafka
Kafka source and channel for consuming events, and Kafka sink for writing events to topics.
Apache HDFS
Primary sink for writing log data to Hadoop Distributed File System for batch analytics.
Apache HBase
HBase sink for writing events directly to HBase tables for random-access analytics.
Apache Solr
Solr sink for indexing log events for full-text search capabilities.
Elasticsearch
Elasticsearch sink for indexing and searching aggregated log data.
Resources
Every other property we hold for Apache Flume — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 2
Pagination, idempotency, versioning, errors, and events
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
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