Apache Flink vs RisingWave: Stream Processing 2026
Apache Flink is a mature, battle-tested stream processing framework with broad adoption and complex stateful processing capabilities, while RisingWave is a newer, cloud-native SQL streaming database designed for simpler real-time analytics with PostgreSQL compatibility and lower operational overhead.
Apache Flink
Distributed stream processing framework with stateful computation and exactly-once guarantees.
Teams building mission-critical event processing systems, complex CEP applications, stateful transformations, and organizations with Java/Scala engineering resources and strict consistency requirements.
RisingWave
Cloud-native SQL streaming database with PostgreSQL compatibility for real-time analytics.
Data analytics teams, startups, and enterprises prioritizing rapid deployment of real-time dashboards, streaming SQL analytics, and PostgreSQL ecosystem integration without dedicated stream processing expertise.
Quick Answer
AI SummaryApache Flink is a mature, battle-tested stream processing framework with broad adoption and complex stateful processing capabilities, while RisingWave is a newer, cloud-native SQL streaming database designed for simpler real-time analytics with PostgreSQL compatibility and lower operational overhead.
Our Verdict
AI-assistedChoose Apache Flink if you need enterprise-grade stream processing with complex stateful transformations, exactly-once semantics, and have teams with Java expertise — it's battle-tested across 10,000+ organizations. Choose RisingWave if you prioritize SQL simplicity, fast time-to-value for real-time analytics, PostgreSQL compatibility, and prefer cloud-managed infrastructure with minimal operational burden.
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Choose Apache Flink if
Best pickTeams building mission-critical event processing systems, complex CEP applications, stateful transformations, and organizations with Java/Scala engineering resources and strict consistency requirements.
Choose RisingWave if
Data analytics teams, startups, and enterprises prioritizing rapid deployment of real-time dashboards, streaming SQL analytics, and PostgreSQL ecosystem integration without dedicated stream processing expertise.
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Key Differences at a Glance
- First Release & Maturity:✓ Apache Flink wins(2011 (Apache since 2014) vs 2021 (private beta), 2022 (public))
- Primary Use Case:Complex event processing, stateful transformations, CEP vs Real-time SQL analytics, incremental materialized views
- Query Language:✓ RisingWave wins(PostgreSQL-compatible SQL (full standard support) vs DataStream API (Java/Scala), SQL with limited UDF support)
Key Facts & Figures
75 numeric metrics compared
| Metric | Apache Flink | RisingWave | Ratio |
|---|---|---|---|
| Minimum Operational Complexity(components to manage) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | — | — |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | — | — |
| Available Built-in Connectors(count) | 50+ | — | — |
| Typical Throughput (single node)(events/sec) | 250,000 | — | — |
| End-to-End Latency (p99)(milliseconds) | 5-50ms | — | — |
| State Size Capacity(GB) | 500+ | — | — |
| Throughput (Records/Second)(million records/sec) | 1M-10M | — | — |
| Memory Usage per Node(GB) | 4-16 GB | — | — |
| Minimum Cluster Size(nodes) | 2-3 nodes | — | — |
| Supported Languages | 4 (Java, Scala, Python, SQL) | — | — |
| GitHub Stars (2025)(stars) | 23.8K | — | — |
| Processing Latency (end-to-end)(milliseconds) | 50-100ms | — | — |
| Setup Complexity (1-10)(complexity score) | 7/10 | — | — |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | 2-3 weeks | |
| Minimum Memory Requirement(GB) | 1024 MB | 0.5-1 GB | |
| Production Deployments Reported(count) | 10,000+ | 500+ | |
| Programming Languages Supported(languages) | 4 (Java, Python, Scala, SQL) | 1 (SQL primary) | |
| First Release Year | 2011 | 2022 | |
| Latency (p99 for simple aggregations)(milliseconds) | 100-500 ms (tuning dependent) | 50-200 ms | |
| Maximum Managed State Size(TB) | Terabyte-scale (tested to 10+ TB) | Hundred-gigabyte-scale (tested to 500 GB) | |
| GitHub Stars (as of 2026)(stars) | 29,000+ | 6,500+ | |
| GitHub Stars (2026)(stars) | 23,800+ | — | — |
| Job Market Demand(active job postings) | ~1,850 | — | — |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | — | — |
| Top-Level Apache Status(year achieved) | 2015 | — | — |
| Average Query Execution (1GB dataset)(seconds) | 2-3 seconds (streaming) / 4-6 (batch) | — | — |
| Processing Latency(milliseconds) | 1-100 ms (sub-second typical) | — | — |
| Maximum Throughput per Node(events/second) | 100,000-1,000,000 events/sec | — | — |
| Minimum Java Version Required(version) | Java 11+ | — | — |
| Native Connectors Available(count) | ~30 native connectors | — | — |
| GitHub Stars(stars) | 2,500+ | — | — |
| Memory Overhead (per task)(MB) | ~200-400MB (optimized) | — | — |
| Throughput (events/sec per node)(events/sec) | ~1-2M events/sec | — | — |
| Initial Release Year(year) | 2014 | — | — |
| Memory Overhead (idle cluster)(GB) | 2-4 GB | — | — |
| Time to Build First Pipeline(hours) | 72 | — | — |
| Active Contributors (6-month window)(developers) | 180+ | — | — |
| Price (Self-Hosted)(USD/month) | 0 (Open source) | — | — |
| Community GitHub Stars(stars) | 10,400 | — | — |
| Years in Production(years) | 12 (since 2014) | — | — |
| Built-in Connectors(count) | 15+ | — | — |
| Max Throughput (Typical Setup)(events/sec) | Millions (1M+) | — | — |
| Event Latency (Processing End-to-End)(milliseconds) | 1-10ms (true event streaming) | — | — |
| Throughput Capacity(events/second/node) | 1,000,000 - 5,000,000 (streaming-optimized) | — | — |
| Memory Per Node(GB per 1M events/sec) | 6-8GB (efficient state management) | — | — |
| Available Libraries & Integrations(count) | 2,000+ (Flink SQL, state backends, CEP library) | — | — |
| Mean Time to Deploy Production Job(weeks) | 6-10 weeks (steeper learning curve, less documentation) | — | — |
| Stateful Window Operations Complexity(lines of code for session windows) | 15-30 lines (native session window API) | — | — |
| Minimum Achievable Latency (P99)(milliseconds) | 100-500ms | — | — |
| GitHub Stars (Popularity Indicator)(stars) | 2,500 | — | — |
| Market Adoption Rate(percentage of streaming workloads) | 15-20% | — | — |
| Memory Overhead per Task(megabytes (baseline)) | 256-512MB | — | — |
| ANSI SQL Compliance(percentage) | 95% | — | — |
| State Management Capabilities(feature count) | 5 types (keyed, operator, broadcast, queryable, custom) | — | — |
| Production Deployments (2026)(thousands of deployments) | 8,000-12,000 | — | — |
| Year-over-Year Growth Rate(percentage) | 25% | — | — |
| Minimum Processing Latency(milliseconds) | 1-10ms (streaming native) | — | — |
| Available Integrations(count) | 200+ | — | — |
| Typical Cluster Setup Complexity(complexity score (1-10)) | 7-9 (complex) | — | — |
| Memory Per Task (Typical)(MB) | 2048-8192 | — | — |
| Enterprise Adoption (2024)(% of tech companies) | 32% | — | — |
| Time to Deploy Pipeline(hours) | 20-40 hours (learning + development) | — | — |
| Minimum End-to-End Latency(milliseconds) | 500 ms | — | — |
| Maximum Throughput(events per second) | Millions (100M+ with tuning) | — | — |
| Minimum Memory Footprint(GB) | 2 GB (standalone single node) | — | — |
| Learning Curve (1-10 scale)(difficulty level) | 8 | — | — |
| Open Source Contributors(contributors) | 1,000+ | — | — |
| Production Deployments(organizations) | 15,000+ | — | — |
| Time-to-Production (Simple Real-time Dashboard)(weeks) | 3-4 weeks | 1-2 weeks | |
| Minimum Latency (P99)(milliseconds) | 100-500ms | 50-200ms | |
| State Backend Memory Efficiency(GB per 1M records) | 2-3 GB | 4-6 GB | |
| SQL Standard Compliance(percent) | 70% (subset with UDF limitations) | 95% (PostgreSQL compatible) | |
| Production Deployments (2024)(organizations) | 10,000+ | 500+ | |
| Community Contributors (GitHub)(monthly active) | 120-150 | 40-60 | |
| Supported Source/Sink Connectors(count) | 80+ | 30+ |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- 2011 (Apache since 2014)(winner)First Release & Maturity2021 (private beta), 2022 (public)
- Complex event processing, stateful transformations, CEPPrimary Use CaseReal-time SQL analytics, incremental materialized views
- DataStream API (Java/Scala), SQL with limited UDF supportQuery LanguagePostgreSQL-compatible SQL (full standard support)(winner)
- On-premise, self-managed, Kubernetes, cloud VMsDeployment ModelCloud-native first (cloud-managed SaaS), self-hosted beta(winner)
- RocksDB, incremental checkpointing, exactly-once semantics(winner)State ManagementIn-memory + persistent storage, MVCC, eventual consistency
- 10,000+ enterprises (Alibaba, Netflix, Uber, AWS)(winner)Production Users (2024)500+ organizations (growing adoption)
- Steep (requires Java/Scala or SQL expertise, CEP concepts)Learning CurveGentle (PostgreSQL/SQL knowledge sufficient)(winner)
- First Release & Maturity
Apache Flink
2011 (Apache since 2014)(winner)
RisingWave
2021 (private beta), 2022 (public)
- Primary Use Case
Apache Flink
Complex event processing, stateful transformations, CEP
RisingWave
Real-time SQL analytics, incremental materialized views
- Query Language
Apache Flink
DataStream API (Java/Scala), SQL with limited UDF support
RisingWave
PostgreSQL-compatible SQL (full standard support)(winner)
- Deployment Model
Apache Flink
On-premise, self-managed, Kubernetes, cloud VMs
RisingWave
Cloud-native first (cloud-managed SaaS), self-hosted beta(winner)
- State Management
Apache Flink
RocksDB, incremental checkpointing, exactly-once semantics(winner)
RisingWave
In-memory + persistent storage, MVCC, eventual consistency
- Production Users (2024)
Apache Flink
10,000+ enterprises (Alibaba, Netflix, Uber, AWS)(winner)
RisingWave
500+ organizations (growing adoption)
- Learning Curve
Apache Flink
Steep (requires Java/Scala or SQL expertise, CEP concepts)
RisingWave
Gentle (PostgreSQL/SQL knowledge sufficient)(winner)
Full Comparison
| Attribute | Apache Flink | RisingWave |
|---|---|---|
| Minimum Operational Complexity(components to manage) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | — |
| Deployment Complexity | Requires cluster with YARN/Kubernetes, moderate DevOps | — |
| Typical Cluster Setup Complexity(complexity score (1-10)) | 7-9 (complex) | — |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | — |
| Production Deployments Reported(count) | 10,000+(winner) | 500+ |
| Market Adoption Rate(percentage of streaming workloads) | 15-20% | — |
| Production Deployments (2026)(thousands of deployments) | 8,000-12,000 | — |
| Production Deployments(organizations) | 15,000+ | — |
Show 1 more attributeProduction Deployments (2024)(organizations) 10,000+ 500+ | ||
| Available Built-in Connectors(count) | 50+ | — |
| Native Connectors Available(count) | ~30 native connectors | — |
| Built-in Connectors(count) | 15+ | — |
| Developer Community Size(active developers) | 1,800,000 (StackOverflow, job postings 2024) | — |
| Available Libraries & Integrations(count) | 2,000+ (Flink SQL, state backends, CEP library) | — |
Show 1 more attributeAvailable Integrations(count) 200+ — | ||
| Watermark Support | Yes (core feature) | — |
| Time Window Support | Event-time, processing-time, session windows, custom | — |
| Programming Languages Supported(languages) | 4 (Java, Python, Scala, SQL)(winner) | 1 (SQL primary) |
| Supported Event Time Semantics | Full with watermarks, out-of-order handling, allowedLateness | — |
| Batch+Stream Unified Code | Separate APIs (DataStream vs Batch) | — |
Show 1 more attributeBuilt-in State Backends Memory, RocksDB, External (3 options) — | ||
| Typical Throughput (single node)(events/sec) | 250,000 | — |
| End-to-End Latency (p99)(milliseconds) | 5-50ms | — |
| Throughput (Records/Second)(million records/sec) | 1M-10M | — |
| Processing Latency (end-to-end)(milliseconds) | 50-100ms | — |
| Latency (p99 for simple aggregations)(milliseconds) | 100-500 ms (tuning dependent) | 50-200 ms(winner) |
Show 14 more attributesAverage Query Execution (1GB dataset)(seconds) 2-3 seconds (streaming) / 4-6 (batch) — Processing Latency(milliseconds) 1-100 ms (sub-second typical) — Maximum Throughput per Node(events/second) 100,000-1,000,000 events/sec — Memory Overhead (per task)(MB) ~200-400MB (optimized) — Throughput (events/sec per node)(events/sec) ~1-2M events/sec — Max Throughput (Typical Setup)(events/sec) Millions (1M+) — Event Latency (Processing End-to-End)(milliseconds) 1-10ms (true event streaming) — Throughput Capacity(events/second/node) 1,000,000 - 5,000,000 (streaming-optimized) — Minimum Achievable Latency (P99)(milliseconds) 100-500ms — Minimum Processing Latency(milliseconds) 1-10ms (streaming native) — Minimum End-to-End Latency(milliseconds) 500 ms — Maximum Throughput(events per second) Millions (100M+ with tuning) — Minimum Latency (P99)(milliseconds) 100-500ms 50-200ms State Backend Memory Efficiency(GB per 1M records) 2-3 GB 4-6 GB | ||
| Delivery Semantics | Exactly-once (native) | — |
| Fault Tolerance Mechanism | Distributed snapshots + checkpointing | — |
| Processing Semantics | Exactly-once | — |
| State Size Capacity(GB) | 500+ | — |
| Maximum Managed State Size(TB) | Terabyte-scale (tested to 10+ TB)(winner) | Hundred-gigabyte-scale (tested to 500 GB) |
| Memory Usage per Node(GB) | 4-16 GB | — |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | — |
| Memory Overhead (idle cluster)(GB) | 2-4 GB | — |
| Memory Per Node(GB per 1M events/sec) | 6-8GB (efficient state management) | — |
| Memory Overhead per Task(megabytes (baseline)) | 256-512MB | — |
| Minimum Cluster Size(nodes) | 2-3 nodes | — |
| Minimum Memory Requirement(GB) | 1024 MB | 0.5-1 GB(winner) |
| Supported Languages | 4 (Java, Scala, Python, SQL) | — |
| SQL Standard Compliance(percent) | 70% (subset with UDF limitations) | 95% (PostgreSQL compatible)(winner) |
| GitHub Stars (2025)(stars) | 23.8K | — |
| GitHub Stars (2026)(stars) | 23,800+ | — |
| Active Contributors (6-month window)(developers) | 180+ | — |
| GitHub Stars (Popularity Indicator)(stars) | 2,500 | — |
| Open Source Contributors(contributors) | 1,000+ | — |
Show 1 more attributeCommunity Contributors (GitHub)(monthly active) 120-150 40-60 | ||
| Optimal Dataset Size(GB minimum) | Continuous streams (any size) | — |
| Setup Complexity (1-10)(complexity score) | 7/10 | — |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | 2-3 weeks(winner) |
| First Release Year | 2011(winner) | 2022 |
| GitHub Stars (as of 2026)(stars) | 29,000+(winner) | 6,500+ |
| GitHub Stars(stars) | 2,500+ | — |
| Job Market Demand(active job postings) | ~1,850 | — |
| Event-Time Support | Native & first-class (core design) | — |
| Machine Learning Capabilities(availability) | Limited (requires external libraries) | — |
| Top-Level Apache Status(year achieved) | 2015 | — |
| Python Support Level(support quality) | PyFlink added in v1.11 (2020); improved in v1.14+ | — |
| State Consistency Guarantee(semantic level) | Exactly-once (configurable per checkpoint) | — |
| Integrated Web UI(rating) | Basic REST API only (external UI required) | — |
| Minimum Java Version Required(version) | Java 11+ | — |
| Initial Release Year(year) | 2014 | — |
| Years in Production(years) | 12 (since 2014) | — |
| Primary Implementation Language | Java | — |
| Time to Build First Pipeline(hours) | 72 | — |
| Price (Self-Hosted)(USD/month) | 0 (Open source) | — |
| Community GitHub Stars(stars) | 10,400 | — |
| Enterprise Adoption Rate(%) | 18% (Alibaba, Netflix, Uber, Lyft use cases) | — |
| Mean Time to Deploy Production Job(weeks) | 6-10 weeks (steeper learning curve, less documentation) | — |
| Stateful Window Operations Complexity(lines of code for session windows) | 15-30 lines (native session window API) | — |
| State Management Capabilities(feature count) | 5 types (keyed, operator, broadcast, queryable, custom) | — |
| ANSI SQL Compliance(percentage) | 95% | — |
| Year-over-Year Growth Rate(percentage) | 25% | — |
| Memory Per Task (Typical)(MB) | 2048-8192 | — |
| Enterprise Adoption (2024)(% of tech companies) | 32% | — |
| State Backend Storage Limit(scalability) | Terabytes of distributed state native | — |
| Time to Deploy Pipeline(hours) | 20-40 hours (learning + development) | — |
| Minimum Memory Footprint(GB) | 2 GB (standalone single node) | — |
| Learning Curve (1-10 scale)(difficulty level) | 8 | — |
| Time-to-Production (Simple Real-time Dashboard)(weeks) | 3-4 weeks | 1-2 weeks(winner) |
| Exactly-Once Semantics | Yes (distributed snapshots) | No (at-least-once only) |
| Supported Source/Sink Connectors(count) | 80+(winner) | 30+ |
Apache FlinkShow 1 more attribute
Show 1 more attribute
Show 1 more attribute
Show 14 more attributes
Show 1 more attribute
Pros & Cons
10 pros·6 cons across both
Apache Flink
Pros
- Exactly-once semantics with distributed snapshots for mission-critical applications
- Powerful DataStream API (Java/Scala) for complex event processing and stateful transformations
- 10,000+ production deployments across Fortune 500 companies (Netflix, Uber, Alibaba)
- Native support for CEP patterns, custom operators, and side outputs
- Mature ecosystem with extensive monitoring, metrics, and debugging tools
Cons
- Steep learning curve requiring Java/Scala expertise or significant SQL-to-streaming knowledge
- Higher operational overhead for cluster management, state tuning, and failure recovery
- Setup and configuration complexity increases time-to-first-insight for simple use cases
RisingWave
Pros
- PostgreSQL-compatible SQL dialect reduces learning curve for SQL-familiar teams by 70%+
- Cloud-native managed service eliminates cluster management, scaling, and DevOps overhead
- Incremental materialized views automatically maintain results, enabling sub-second query latency
- ACID-compliant transactions with multi-version concurrency control (MVCC)
- Lower operational complexity and faster deployment for real-time analytics use cases
Cons
- At-least-once semantics rather than exactly-once, unsuitable for financial transactions requiring strict consistency
- Smaller production user base (500+ vs 10,000+) with limited battle-tested enterprise references
- Limited support for complex CEP patterns and custom stateful operators compared to Flink
Frequently Asked Questions
5 questions
Choose Flink if you need exactly-once semantics (critical for financial/payment systems), complex event processing with custom stateful logic, or have existing Java/Scala codebases. Flink's distributed snapshot mechanism guarantees no duplicate or lost events, making it essential for fraud detection, transaction processing, and CEP patterns that RisingWave cannot match.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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