Apache Livy
Apache Livy is a service that enables easy interaction with a Spark cluster over a REST interface. It allows submitting Spark jobs or snippets of Spark code, retrieving results synchronously or asynchronously, and managing Spark contexts across multiple users. Licensed under Apache 2.0.
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 Livy the way a machine reads it — 64 machine-readable artifacts across 3 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 Livy scores 54.7/100 (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 Livy
Each block below is one kind of artifact we hold for Apache Livy. 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 3
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 Livy Batches API
Batch Spark job submission
Apache Livy Sessions API
Interactive Spark session management
Apache Livy Statements API
Code statement execution within sessions
Pricing 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 Livy 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 Livy Finops
FINOPSFeatures 7
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.
Interactive Spark Sessions
Create persistent Spark contexts for interactive code execution in Python, Scala, R, and SQL.
Batch Job Submission
Submit batch Spark jobs without creating an interactive session.
Multi-Language Support
Execute code in PySpark, Spark (Scala), SparkR, and SQL.
Multi-User Impersonation
Proxy user support for multi-tenant Spark cluster access.
Asynchronous Execution
Submit jobs and poll for results asynchronously.
Log Access
Retrieve driver and executor logs for debugging.
REST Interface
Simple HTTP REST API for Spark cluster interaction without native clients.
Scroll within the panel for all 7 ·
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.
Apache Livy Rest Api Context
JSON-LDSpectral 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 Livy API Rules
SPECTRALApache Livy API Rules
SPECTRALJSON Schema 12
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.
BatchList
JSON SCHEMABatch
JSON SCHEMABatchState
JSON SCHEMACreateBatchRequest
JSON SCHEMACreateSessionRequest
JSON SCHEMALog
JSON SCHEMASessionList
JSON SCHEMASession
JSON SCHEMASessionState
JSON SCHEMAStatementList
JSON SCHEMAStatementRequest
JSON SCHEMAStatement
JSON SCHEMAScroll within the panel for all 12 ·
JSON Structure 12
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.
Rest Api Batch List Structure
JSON STRUCTURERest Api Batch State Structure
JSON STRUCTURERest Api Batch Structure
JSON STRUCTURERest Api Create Batch Request Structure
JSON STRUCTURERest Api Create Session Request Structure
JSON STRUCTURERest Api Log Structure
JSON STRUCTURERest Api Session List Structure
JSON STRUCTURERest Api Session State Structure
JSON STRUCTURERest Api Session Structure
JSON STRUCTURERest Api Statement List Structure
JSON STRUCTURERest Api Statement Request Structure
JSON STRUCTURERest Api Statement Structure
JSON STRUCTUREScroll within the panel for all 12 ·
Examples 12
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.
Rest Api Batch Example
EXAMPLERest Api Batch List Example
EXAMPLERest Api Batch State Example
EXAMPLERest Api Log Example
EXAMPLERest Api Session Example
EXAMPLERest Api Statement Example
EXAMPLEScroll within the panel for all 12 ·
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 4
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.
Notebook Integration
Power Jupyter, Zeppelin, and other notebooks with Spark backends via Livy.
Data Engineering Pipelines
Submit Spark batch jobs from orchestration tools like Airflow and Oozie.
Interactive Data Exploration
Execute ad-hoc Spark code for exploratory data analysis.
Multi-Tenant Spark Access
Enable multiple users to share a Spark cluster with isolation via Livy sessions.
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 Spark
Livy requires a Spark cluster and acts as the REST gateway to Spark.
Apache Zeppelin
Zeppelin notebook backend using Livy for distributed Spark execution.
Jupyter Notebook
Jupyter sparkmagic extension uses Livy for remote Spark kernel access.
Apache Airflow
Airflow LivyOperator for submitting Spark batch jobs from DAGs.
Amazon EMR
Livy is available as an EMR application for REST-based Spark access.
Resources
Every other property we hold for Apache Livy — 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 3
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
Commercial 1
Pricing, plans, and the legal terms of use
← All providers · Data indexed from github.com/api-evangelist/apache-livy · machine-readable index on apis.io