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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Apache Flink Masterclass — Stateful Stream Processing</title>
<meta property="og:title" content="Apache Flink Masterclass — Stateful Stream Processing">
<meta property="og:description" content="Master Apache Flink — stateful stream processing, event-time windows, and distributed data pipelines at scale.">
<meta property="og:image" content="../mathsGraph.jpg">
<meta property="og:type" content="article">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link href="https://fonts.googleapis.com/css2?family=Syncopate:wght@400;700&family=Share+Tech+Mono&family=Nunito:wght@300;400;500;600&display=swap" rel="stylesheet">
<style>
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--mono: 'Share Tech Mono', monospace;
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--display: 'Syncopate', sans-serif;
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html { scroll-behavior: smooth; }
body {
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font-size: 14.5px;
line-height: 1.7;
min-height: 100vh;
overflow-x: hidden;
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/* ── LAVA FLOW BACKGROUND ── */
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pointer-events: none; z-index: 0;
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/* ── HEADER ── */
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@keyframes zap { 0%,90%,100%{opacity:1;transform:scale(1)} 92%{opacity:0.4;transform:scale(0.9)} 94%{opacity:1;transform:scale(1.1)} 96%{opacity:0.6;transform:scale(0.95)} }
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/* Syntax */
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.op-row { display: flex; align-items: center; gap: 6px; margin: 4px 0; flex-wrap: wrap; }
.op-box {
padding: 6px 14px; border: 1px solid; font-family: var(--mono);
font-size: 0.72rem; display: inline-flex; align-items: center; gap: 6px;
transition: all 0.15s; white-space: nowrap;
}
.op-box:hover { transform: translateY(-1px); }
.ob-source { background: var(--teal-dim); border-color: var(--teal); color: var(--teal); }
.ob-map { background: var(--amb-dim); border-color: var(--amber); color: var(--amber); }
.ob-filter { background: var(--crim-dim); border-color: var(--crim2); color: var(--crim3); }
.ob-key { background: var(--gold-dim); border-color: var(--gold); color: var(--gold); }
.ob-window { background: var(--slate-dim); border-color: var(--slate); color: var(--slate); }
.ob-agg { background: rgba(208,80,232,0.12); border-color: #d050e8; color: #d050e8; }
.ob-sink { background: var(--magma4); border-color: var(--text3); color: var(--text2); }
.ob-state { background: rgba(255,209,102,0.12); border-color: var(--gold); color: var(--gold); }
.op-arr { color: var(--text3); font-family: var(--mono); font-size: 0.85rem; flex-shrink: 0; }
.op-note { font-family: var(--mono); font-size: 0.62rem; color: var(--text3); }
/* ── WATERMARK TIMELINE ── */
.wm-track {
background: rgba(0,0,0,0.4); border: 1px solid var(--border);
padding: 1rem; margin: 1rem 0; overflow-x: auto;
}
.wm-row { display: flex; align-items: flex-end; gap: 4px; margin-bottom: 0.5rem; min-width: 600px; }
.wm-event {
width: 36px; display: flex; flex-direction: column; align-items: center; gap: 2px;
}
.wm-bubble {
width: 32px; height: 32px; border: 1px solid; border-radius: 1px;
display: flex; align-items: center; justify-content: center;
font-family: var(--mono); font-size: 0.58rem; font-weight: 700;
transition: all 0.15s; cursor: default; flex-shrink: 0;
}
.wm-bubble:hover { transform: scale(1.1); }
.wm-ts { font-family: var(--mono); font-size: 0.52rem; color: var(--text3); text-align: center; }
.wmb-in-order { background: var(--teal-dim); border-color: var(--teal); color: var(--teal); }
.wmb-late { background: var(--crim-dim); border-color: var(--crim2); color: var(--crim3); }
.wmb-watermark { background: var(--amb-dim); border-color: var(--amber); color: var(--amber); width: 48px; height: 28px; }
.wmb-allowed { background: rgba(245,166,35,0.1); border-color: rgba(245,166,35,0.3); color: var(--text3); }
.wm-label { font-family: var(--mono); font-size: 0.62rem; color: var(--text2); margin-bottom: 4px; }
/* ── CHECKPOINT DIAGRAM ── */
.ckpt-row { display: flex; align-items: center; gap: 8px; margin: 6px 0; }
.ckpt-op {
padding: 5px 12px; border: 1px solid var(--border);
font-family: var(--mono); font-size: 0.68rem; color: var(--text2);
background: var(--magma3); min-width: 80px; text-align: center;
}
.ckpt-barrier {
padding: 5px 8px; border: 1px dashed var(--amber);
font-family: var(--mono); font-size: 0.6rem; color: var(--amber);
background: var(--amb-dim); letter-spacing: 0.06em;
}
.ckpt-state {
padding: 5px 10px; border: 1px solid var(--gold);
font-family: var(--mono); font-size: 0.6rem; color: var(--gold);
background: var(--gold-dim);
}
.ckpt-arr { color: var(--text3); font-size: 0.8rem; }
/* ── COMPARISON GRID ── */
.vs-grid {
display: grid; font-size: 0.8rem;
grid-template-columns: 180px repeat(2,1fr);
gap: 1px; background: var(--border); border: 1px solid var(--border);
margin: 1rem 0;
}
.vs-cell {
background: var(--magma2); padding: 0.65rem 0.9rem;
font-family: var(--mono); font-size: 0.71rem;
}
.vs-head { background: var(--magma4); font-weight: 700; letter-spacing: 0.06em; }
.vs-label { color: var(--text3); letter-spacing: 0.06em; text-transform: uppercase; font-size: 0.63rem; }
.vs-flink { background: var(--crim-dim) !important; border-left: 2px solid var(--crim); }
.vs-kafka { background: rgba(0,201,167,0.04) !important; }
.vs-win { color: var(--teal); }
.vs-lose { color: var(--crim3); }
.vs-tie { color: var(--amber); }
/* ── PERF ROW ── */
.perf-row { display: grid; grid-template-columns: 200px 1fr 100px; gap: 1rem; align-items: center; padding: 0.55rem 0; border-bottom: 1px solid var(--border2); }
.perf-label { font-family: var(--mono); font-size: 0.73rem; color: var(--text2); }
.perf-track { height: 7px; background: var(--magma4); border: 1px solid var(--border); overflow: hidden; }
.perf-fill { height: 100%; transition: width 1.2s cubic-bezier(.4,0,.2,1); }
.perf-val { font-family: var(--mono); font-size: 0.7rem; color: var(--text3); text-align: right; }
/* ── SEP ── */
.sep { border: none; border-top: 1px solid var(--border); margin: 2rem 0; }
.sep-fire { border-top: 1px solid; border-image: linear-gradient(90deg,var(--crim),rgba(232,54,10,0.15),transparent) 1; }
/* ── TEXT ── */
p { margin-bottom: 0.9rem; color: var(--text2); font-weight: 300; }
p:last-child { margin-bottom: 0; }
h3 { font-family: var(--display); font-size: 1.4rem; font-weight: 700; letter-spacing: 0.06em; color: var(--text); margin: 1.75rem 0 0.6rem; }
h3:first-child { margin-top: 0; }
h4 { font-family: var(--mono); font-size: 0.72rem; color: var(--text3); letter-spacing: 0.12em; text-transform: uppercase; margin: 1.2rem 0 0.5rem; }
ul,ol { padding-left: 1.5rem; color: var(--text2); font-weight: 300; }
li { margin-bottom: 0.35rem; }
strong { color: var(--text); font-weight: 600; }
::-webkit-scrollbar { width: 5px; height: 5px; }
::-webkit-scrollbar-track { background: var(--magma2); }
::-webkit-scrollbar-thumb { background: var(--magma4); }
</style>
</head>
<body>
<nav style="font-family:'DM Mono',monospace;font-size:0.7rem;padding:8px 16px;background:#1c1a16;color:#b8b0a4;border-bottom:1px solid #333;letter-spacing:0.03em;position:sticky;top:0;z-index:9999;">
<a href="../index.html" style="color:#cc4400;text-decoration:none;">KeGG</a>
<span style="color:#555;margin:0 6px;">/</span>
<span style="color:#b8b0a4;">Masterclasses</span>
<span style="color:#555;margin:0 6px;">/</span>
<span style="color:#f2ece0;">Apache Flink Masterclass — Stateful Stream Processing</span>
</nav>
<header>
<div class="header-inner">
<div class="logo">
<div class="logo-mark"></div>
<div>
<div class="logo-text">FLINK</div>
<div class="logo-sub">Stateful Stream Processing</div>
</div>
</div>
<nav>
<button class="nav-btn active" onclick="show('arch',this)">ARCH</button>
<button class="nav-btn" onclick="show('datastream',this)">DATASTREAM</button>
<button class="nav-btn" onclick="show('watermarks',this)">WATERMARKS</button>
<button class="nav-btn" onclick="show('state',this)">STATE</button>
<button class="nav-btn" onclick="show('sql',this)">FLINK SQL</button>
<button class="nav-btn" onclick="show('windows',this)">WINDOWS</button>
<button class="nav-btn" onclick="show('eos',this)">EOS</button>
<button class="nav-btn" onclick="show('vs',this)">FLINK vs KAFKA</button>
</nav>
<div class="hdr-badge">
<div class="ver-tag">v1.19 / v2.0</div>
</div>
</div>
</header>
<div class="wrapper">
<!-- ══════════════════════════════════════════
SECTION 1 — ARCHITECTURE
══════════════════════════════════════════ -->
<section id="arch" class="active">
<div class="section-header">
<div class="section-eyebrow">Runtime Architecture</div>
<div class="section-title">THE STREAMING <em>ENGINE</em></div>
<div class="section-sub">Flink is a distributed stateful computation engine. Where Kafka moves data between systems, Flink computes on that data in motion — with stateful operators, event-time semantics, and fault-tolerant exactly-once processing baked all the way down to the execution model.</div>
</div>
<div class="section-rule"></div>
<div class="stat-strip">
<div class="stat"><div class="stat-num" style="color:var(--crim3)">JM+TM</div><div class="stat-label">Job + Task Mgr</div></div>
<div class="stat"><div class="stat-num" style="color:var(--amber)">Slots</div><div class="stat-label">Unit of parallelism</div></div>
<div class="stat"><div class="stat-num" style="color:var(--teal)">Async</div><div class="stat-label">Checkpointing</div></div>
<div class="stat"><div class="stat-num" style="color:var(--gold)">RocksDB</div><div class="stat-label">State Backend</div></div>
<div class="stat"><div class="stat-num" style="color:var(--crim3)">ms</div><div class="stat-label">End-to-end Latency</div></div>
<div class="stat"><div class="stat-num" style="color:var(--teal)">Unified</div><div class="stat-label">Batch + Stream API</div></div>
</div>
<div class="g2">
<div>
<h3>Cluster Topology</h3>
<div class="card" style="padding:1.25rem">
<div class="card-label">Runtime components</div>
<!-- Client layer -->
<div class="op-row">
<div class="op-box ob-map" style="width:100%;justify-content:center">CLIENT — submits JobGraph via REST / CLI / SDK</div>
</div>
<div class="op-row" style="justify-content:center"><div class="op-arr">↓ JobGraph</div></div>
<!-- JM -->
<div class="op-box ob-source" style="width:100%;justify-content:center">
JOB MANAGER — Dispatcher + ResourceManager + JobMaster
</div>
<div style="display:flex;gap:4px;margin:2px 0;font-family:var(--mono);font-size:0.62rem;color:var(--text3)">
<span style="flex:1;text-align:center">▸ Schedules tasks</span>
<span style="flex:1;text-align:center">▸ Coordinates checkpoints</span>
<span style="flex:1;text-align:center">▸ Recovery on failure</span>
</div>
<div class="op-row" style="justify-content:center"><div class="op-arr">↓ Deploy subtasks</div></div>
<!-- TMs -->
<div style="display:grid;grid-template-columns:1fr 1fr 1fr;gap:4px">
<div class="op-box ob-sink" style="flex-direction:column;align-items:flex-start;gap:2px">
<span style="color:var(--text)">TASKMANAGER 1</span>
<span style="font-size:0.6rem;color:var(--text3)">Slot 1 | Slot 2 | Slot 3</span>
<span style="font-size:0.58rem;color:var(--teal-dim)">JVM process → threads</span>
</div>
<div class="op-box ob-sink" style="flex-direction:column;align-items:flex-start;gap:2px">
<span style="color:var(--text)">TASKMANAGER 2</span>
<span style="font-size:0.6rem;color:var(--text3)">Slot 1 | Slot 2 | Slot 3</span>
<span style="font-size:0.58rem;color:var(--teal-dim)">Shares JVM heap + off-heap</span>
</div>
<div class="op-box ob-sink" style="flex-direction:column;align-items:flex-start;gap:2px">
<span style="color:var(--text)">TASKMANAGER N</span>
<span style="font-size:0.6rem;color:var(--text3)">Slot 1 | Slot 2 | Slot 3</span>
<span style="font-size:0.58rem;color:var(--teal-dim)">RocksDB for keyed state</span>
</div>
</div>
<div class="op-arr" style="text-align:center;margin:4px 0">↓ Persist snapshots</div>
<div class="op-box ob-state" style="width:100%;justify-content:center">
STATE BACKEND — HDFS / S3 / GCS (checkpoint storage)
</div>
</div>
<div class="callout c-crim">
<strong>Slot — The Unit of Parallelism</strong>
Each TaskManager is a JVM process with <em>N</em> slots. A slot = a fixed slice of CPU and memory. An operator with parallelism 4 occupies 4 slots across TaskManagers. <strong>Slot sharing</strong> (default) lets different operators in the same job share a slot — enabling a full pipeline to run in one slot per TaskManager without extra scheduling overhead.
</div>
</div>
<div>
<h3>Execution Model: Dataflow Graph</h3>
<p>A Flink program compiles to a <strong>Logical Dataflow Graph</strong> (operators + streams), then to a <strong>Physical Execution Graph</strong> (parallel subtasks). Network buffers connect subtasks — data flows through them in a push model, not a pull-loop like Kafka consumers.</p>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">Logical → Physical execution</span><span class="code-lang">concept</span></div>
<pre><span class="cm">/* LOGICAL GRAPH (your code):
Source ──→ Map ──→ KeyBy ──→ Window ──→ Sink
PHYSICAL GRAPH (parallelism=3, what actually runs):
Source[0] ──→ Map[0] ──→\
Source[1] ──→ Map[1] ──→ KeyBy/shuffle ──→ Window[0] ──→ Sink[0]
Source[2] ──→ Map[2] ──→/ ──→ Window[1] ──→ Sink[1]
──→ Window[2] ──→ Sink[2]
NETWORK SHUFFLE: KeyBy routes each record to exactly one
downstream subtask based on hash(key) % parallelism.
This is the only place data crosses thread/network boundaries.
FORWARD CHANNELS: Source→Map (same parallelism) use direct
buffer handoff — zero serialization, zero network copy.
*/</span>
<span class="cm">// Configure execution mode:</span>
<span class="ty">StreamExecutionEnvironment</span> env <span class="op">=</span>
<span class="ty">StreamExecutionEnvironment</span>.getExecutionEnvironment();
env.setParallelism(<span class="nu">4</span>); <span class="cm">// global default</span>
env.disableOperatorChaining(); <span class="cm">// force separate tasks (debugging)</span>
env.setBufferTimeout(<span class="nu">100</span>); <span class="cm">// flush network buffers every 100ms</span>
<span class="cm">// lower = latency, higher = throughput</span>
<span class="cm">// Per-operator parallelism override:</span>
stream
.map(fn).setParallelism(<span class="nu">8</span>) <span class="cm">// this operator uses 8 subtasks</span>
.keyBy(key) <span class="cm">// shuffle happens here</span>
.window(...)
.sum(<span class="st">"amount"</span>).setParallelism(<span class="nu">4</span>);</pre>
</div>
<h3>Deployment Modes</h3>
<table>
<thead><tr><th>Mode</th><th>JobManager</th><th>Use Case</th></tr></thead>
<tbody>
<tr><td>Application</td><td>Runs inside cluster, 1 JM per job</td><td>Production — strong isolation</td></tr>
<tr><td>Per-Job</td><td>Created on submit, destroyed on exit</td><td>YARN/K8s short-lived jobs</td></tr>
<tr><td>Session</td><td>Long-lived, shared by many jobs</td><td>Dev, low-latency deploys</td></tr>
<tr><td>Local</td><td>Embedded in main() process</td><td>Testing, IDE debugging</td></tr>
</tbody>
</table>
</div>
</div>
</section>
<!-- ══════════════════════════════════════════
SECTION 2 — DATASTREAM API
══════════════════════════════════════════ -->
<section id="datastream">
<div class="section-header">
<div class="section-eyebrow">Core Processing API</div>
<div class="section-title">DATASTREAM <em>API</em></div>
<div class="section-sub">The DataStream API is Flink's low-level streaming primitive. It gives you full control over state, timers, watermarks, and side outputs. Every higher-level API (Table, SQL, PyFlink) compiles down to DataStream operators.</div>
</div>
<div class="section-rule"></div>
<div class="pills" id="ds-pills">
<button class="pill active" onclick="showTab('ds','java',this)">Java / Scala</button>
<button class="pill" onclick="showTab('ds','python',this)">PyFlink</button>
<button class="pill" onclick="showTab('ds','process',this)">ProcessFunction</button>
<button class="pill" onclick="showTab('ds','sideout',this)">Side Outputs</button>
</div>
<!-- JAVA -->
<div id="ds-java" class="tab-pane active">
<div class="g2">
<div class="code-wrap">
<div class="code-header"><span class="code-fname">Full streaming job — Java DSL</span><span class="code-lang">java</span></div>
<pre><span class="ty">StreamExecutionEnvironment</span> env <span class="op">=</span>
<span class="ty">StreamExecutionEnvironment</span>.getExecutionEnvironment();
<span class="cm">// ── SOURCE ──────────────────────────────────────</span>
<span class="ty">KafkaSource</span><<span class="ty">Order</span>> source <span class="op">=</span> <span class="ty">KafkaSource</span>.<<span class="ty">Order</span>>builder()
.setBootstrapServers(<span class="st">"broker:9092"</span>)
.setTopics(<span class="st">"orders"</span>)
.setGroupId(<span class="st">"flink-order-processor"</span>)
.setStartingOffsets(<span class="ty">OffsetsInitializer</span>.earliest())
.setValueOnlyDeserializer(<span class="kw">new</span> <span class="ty">OrderDeserializer</span>())
.build();
<span class="ty">DataStream</span><<span class="ty">Order</span>> orders <span class="op">=</span> env.fromSource(
source,
<span class="ty">WatermarkStrategy</span>
.<<span class="ty">Order</span>>forBoundedOutOfOrderness(<span class="ty">Duration</span>.ofSeconds(<span class="nu">5</span>))
.withTimestampAssigner((e, ts) <span class="op">-></span> e.getEventTs()),
<span class="st">"Kafka Orders Source"</span>
);
<span class="cm">// ── TRANSFORMATIONS ─────────────────────────────</span>
<span class="ty">DataStream</span><<span class="ty">RevenueMetric</span>> revenue <span class="op">=</span> orders
.filter(o <span class="op">-></span> o.getStatus() <span class="op">==</span> <span class="ty">Status</span>.COMPLETED) <span class="cm">// parallel filter</span>
.map(o <span class="op">-></span> <span class="kw">new</span> <span class="ty">EnrichedOrder</span>(o, enrich(o))) <span class="cm">// stateless map</span>
.keyBy(<span class="ty">EnrichedOrder</span>::getCustomerId) <span class="cm">// hash shuffle</span>
.window(<span class="ty">TumblingEventTimeWindows</span>.of(<span class="ty">Time</span>.hours(<span class="nu">1</span>))) <span class="cm">// 1-hour window</span>
.aggregate(
<span class="kw">new</span> <span class="ty">RevenueAggregator</span>(), <span class="cm">// incremental: O(1) state per window</span>
<span class="kw">new</span> <span class="ty">WindowResultMapper</span>() <span class="cm">// called once when window fires</span>
);
<span class="cm">// ── SINK ─────────────────────────────────────────</span>
<span class="ty">KafkaSink</span><<span class="ty">RevenueMetric</span>> sink <span class="op">=</span> <span class="ty">KafkaSink</span>.<<span class="ty">RevenueMetric</span>>builder()
.setBootstrapServers(<span class="st">"broker:9092"</span>)
.setRecordSerializer(<span class="ty">KafkaRecordSerializationSchema</span>.builder()
.setTopic(<span class="st">"hourly-revenue"</span>)
.setValueSerializationSchema(<span class="kw">new</span> <span class="ty">RevenueSerializer</span>())
.build())
.setDeliveryGuarantee(<span class="ty">DeliveryGuarantee</span>.EXACTLY_ONCE) <span class="cm">// 2PC</span>
.setTransactionalIdPrefix(<span class="st">"revenue-sink"</span>)
.build();
revenue.sinkTo(sink);
env.execute(<span class="st">"Hourly Revenue Aggregator"</span>);</pre>
</div>
<div>
<div class="op-row"><div class="op-box ob-source">KafkaSource</div><div class="op-arr">→</div><div class="op-note">assigns event timestamps + watermarks</div></div>
<div class="op-row" style="padding-left:1rem"><div class="op-arr">↓</div></div>
<div class="op-row"><div class="op-box ob-filter">filter</div><div class="op-arr">→</div><div class="op-note">stateless, executes in same task chain</div></div>
<div class="op-row" style="padding-left:1rem"><div class="op-arr">↓</div></div>
<div class="op-row"><div class="op-box ob-map">map (enrich)</div><div class="op-arr">→</div><div class="op-note">operator chaining — zero serialization</div></div>
<div class="op-row" style="padding-left:1rem"><div class="op-arr">↓ hash shuffle (network)</div></div>
<div class="op-row"><div class="op-box ob-key">keyBy(customerId)</div><div class="op-arr">→</div><div class="op-note">same key always reaches same subtask</div></div>
<div class="op-row" style="padding-left:1rem"><div class="op-arr">↓</div></div>
<div class="op-row"><div class="op-box ob-window">TumblingEventTime(1h)</div><div class="op-arr">→</div><div class="op-note">window buffers until watermark passes window end</div></div>
<div class="op-row" style="padding-left:1rem"><div class="op-arr">↓ fires on watermark</div></div>
<div class="op-row"><div class="op-box ob-agg">aggregate (incremental)</div><div class="op-arr">→</div><div class="op-note">AggregateFunction accumulates state per-window</div></div>
<div class="op-row" style="padding-left:1rem"><div class="op-arr">↓</div></div>
<div class="op-row"><div class="op-box ob-sink">KafkaSink (EOS)</div><div class="op-arr">→</div><div class="op-note">2-phase commit with checkpoint barrier</div></div>
<div class="callout c-teal" style="margin-top:1rem">
<strong>Operator Chaining — The Performance Secret</strong>
Flink fuses consecutive stateless operators (filter→map→flatMap) into a single task thread. No serialization, no network, no buffer — just method calls in a tight loop. This is often the single largest source of latency reduction vs naive Kafka Streams topologies.
</div>
</div>
</div>
</div>
<!-- PYFLINK -->
<div id="ds-python" class="tab-pane">
<div class="g2">
<div class="code-wrap">
<div class="code-header"><span class="code-fname">PyFlink DataStream API</span><span class="code-lang">python</span></div>
<pre><span class="py">from</span> pyflink.datastream <span class="py">import</span> StreamExecutionEnvironment, RuntimeExecutionMode
<span class="py">from</span> pyflink.datastream.connectors.kafka <span class="py">import</span> (
KafkaSource, KafkaOffsetsInitializer, KafkaRecordSerializationSchema, KafkaSink
)
<span class="py">from</span> pyflink.common <span class="py">import</span> WatermarkStrategy, Duration, Types
<span class="py">from</span> pyflink.common.serialization <span class="py">import</span> SimpleStringSchema
env <span class="op">=</span> StreamExecutionEnvironment.get_execution_environment()
env.set_parallelism(<span class="nu">4</span>)
env.enable_checkpointing(<span class="nu">60_000</span>) <span class="cm"># checkpoint every 60s</span>
<span class="cm"># Add Kafka connector JARs (needed for PyFlink)</span>
env.add_jars(<span class="st">"file:///opt/flink/lib/flink-connector-kafka.jar"</span>)
<span class="cm"># ── SOURCE ──────────────────────────────────────</span>
source <span class="op">=</span> (
KafkaSource.builder()
.set_bootstrap_servers(<span class="st">"broker:9092"</span>)
.set_topics(<span class="st">"orders"</span>)
.set_group_id(<span class="st">"flink-py-processor"</span>)
.set_starting_offsets(KafkaOffsetsInitializer.earliest())
.set_value_only_deserializer(SimpleStringSchema())
.build()
)
orders <span class="op">=</span> env.from_source(
source,
WatermarkStrategy
.for_bounded_out_of_orderness(Duration.of_seconds(<span class="nu">5</span>))
.with_idleness(Duration.of_minutes(<span class="nu">1</span>)),
<span class="st">"Kafka Source"</span>
)
<span class="cm"># ── TRANSFORMATIONS ─────────────────────────────</span>
<span class="py">import</span> json
<span class="py">def</span> <span class="fn">parse_order</span>(raw_json: str) -> dict:
d <span class="op">=</span> json.loads(raw_json)
<span class="py">return</span> (d[<span class="st">'customer_id'</span>], d[<span class="st">'amount'</span>])
<span class="py">def</span> <span class="fn">is_valid</span>(record) -> bool:
<span class="py">return</span> record[<span class="nu">1</span>] <span class="op">></span> <span class="nu">0</span>
parsed <span class="op">=</span> (
orders
.map(parse_order, output_type<span class="op">=</span>Types.TUPLE([Types.STRING(), Types.FLOAT()]))
.filter(is_valid)
.key_by(<span class="py">lambda</span> x: x[<span class="nu">0</span>]) <span class="cm"># key by customer_id</span>
)
<span class="cm"># ── STATEFUL REDUCE (sum per customer, unbounded) ──</span>
result <span class="op">=</span> parsed.reduce(<span class="py">lambda</span> a, b: (a[<span class="nu">0</span>], a[<span class="nu">1</span>] <span class="op">+</span> b[<span class="nu">1</span>]))
result.print()
env.execute(<span class="st">"PyFlink Order Processor"</span>)</pre>
</div>
<div>
<div class="callout c-amber">
<strong>PyFlink Architecture — JVM Bridge</strong>
PyFlink is not pure Python. It's a Python wrapper around the Java/Scala Flink runtime. Python operators use Apache Beam's Python worker model — your Python functions run in a separate process connected to the JVM via gRPC. This means: Python UDFs add ~2-5ms per-record overhead vs Java. For heavy Python workloads, prefer Table API / Flink SQL which push computation to the JVM.
</div>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">PyFlink — Table API (recommended for Python)</span><span class="code-lang">python</span></div>
<pre><span class="py">from</span> pyflink.table <span class="py">import</span> EnvironmentSettings, TableEnvironment
<span class="py">from</span> pyflink.table.expressions <span class="py">import</span> col, lit
settings <span class="op">=</span> EnvironmentSettings.in_streaming_mode()
t_env <span class="op">=</span> TableEnvironment.create(settings)
<span class="cm"># Define source with DDL (connector resolved at runtime)</span>
t_env.execute_sql(<span class="st">"""
CREATE TABLE orders (
order_id STRING,
customer_id STRING,
amount DOUBLE,
event_ts TIMESTAMP(3),
WATERMARK FOR event_ts AS event_ts - INTERVAL '5' SECOND
) WITH (
'connector' = 'kafka',
'topic' = 'orders',
'properties.bootstrap.servers' = 'broker:9092',
'format' = 'json'
)
"""</span>)
<span class="cm"># Table API: compiled to Java operators, zero Python overhead</span>
orders_table <span class="op">=</span> t_env.from_path(<span class="st">"orders"</span>)
result <span class="op">=</span> (
orders_table
.filter(col(<span class="st">"amount"</span>) <span class="op">></span> <span class="nu">0</span>)
.group_by(col(<span class="st">"customer_id"</span>))
.select(
col(<span class="st">"customer_id"</span>),
col(<span class="st">"amount"</span>).sum.alias(<span class="st">"total_spend"</span>)
)
)
result.execute().print()</pre>
</div>
</div>
</div>
</div>
<!-- PROCESS FUNCTION -->
<div id="ds-process" class="tab-pane">
<div class="g2">
<div class="code-wrap">
<div class="code-header"><span class="code-fname">ProcessFunction — full low-level control</span><span class="code-lang">java</span></div>
<pre><span class="cm">/**
* ProcessFunction: the most powerful Flink primitive.
* Gives you:
* - Access to keyed state (per-key values, lists, maps)
* - Event-time and processing-time timers
* - Side outputs (route to different streams)
* - Full context: timestamp, key, current watermark
*/</span>
<span class="kw">public class</span> <span class="ty">SessionDetector</span>
<span class="kw">extends</span> <span class="ty">KeyedProcessFunction</span><<span class="ty">String</span>, <span class="ty">Event</span>, <span class="ty">Session</span>> {
<span class="cm">// Declared state — Flink manages lifecycle across checkpoints</span>
<span class="kw">private</span> <span class="ty">ValueState</span><<span class="ty">Long</span>> sessionStart;
<span class="kw">private</span> <span class="ty">ValueState</span><<span class="ty">Long</span>> lastEventTs;
<span class="kw">private</span> <span class="ty">ValueState</span><<span class="ty">Integer</span>> eventCount;
<span class="kw">private</span> <span class="ty">ValueState</span><<span class="ty">Long</span>> timerTs; <span class="cm">// pending timer</span>
<span class="kw">private static final long</span> GAP <span class="op">=</span> <span class="nu">30</span> <span class="op">*</span> <span class="nu">60_000</span>; <span class="cm">// 30min inactivity</span>
<span class="an">@Override</span>
<span class="kw">public void</span> <span class="fn">open</span>(<span class="ty">OpenContext</span> ctx) {
sessionStart <span class="op">=</span> getRuntimeContext().getState(
<span class="kw">new</span> <span class="ty">ValueStateDescriptor</span><>(<span class="st">"session-start"</span>, <span class="ty">Long</span>.<span class="kw">class</span>));
lastEventTs <span class="op">=</span> getRuntimeContext().getState(
<span class="kw">new</span> <span class="ty">ValueStateDescriptor</span><>(<span class="st">"last-event"</span>, <span class="ty">Long</span>.<span class="kw">class</span>));
eventCount <span class="op">=</span> getRuntimeContext().getState(
<span class="kw">new</span> <span class="ty">ValueStateDescriptor</span><>(<span class="st">"event-count"</span>, <span class="ty">Integer</span>.<span class="kw">class</span>));
timerTs <span class="op">=</span> getRuntimeContext().getState(
<span class="kw">new</span> <span class="ty">ValueStateDescriptor</span><>(<span class="st">"timer-ts"</span>, <span class="ty">Long</span>.<span class="kw">class</span>));
}
<span class="an">@Override</span>
<span class="kw">public void</span> <span class="fn">processElement</span>(<span class="ty">Event</span> e, <span class="ty">Context</span> ctx, <span class="ty">Collector</span><<span class="ty">Session</span>> out) {
<span class="ty">Long</span> start <span class="op">=</span> sessionStart.value();
<span class="kw">if</span> (start <span class="op">==</span> <span class="kw">null</span>) {
sessionStart.update(e.ts); <span class="cm">// start of new session</span>
eventCount.update(<span class="nu">0</span>);
}
eventCount.update(eventCount.value() <span class="op">+</span> <span class="nu">1</span>);
lastEventTs.update(e.ts);
<span class="cm">// Cancel old timer, register new one (30min from NOW)</span>
<span class="ty">Long</span> oldTimer <span class="op">=</span> timerTs.value();
<span class="kw">if</span> (oldTimer <span class="op">!=</span> <span class="kw">null</span>)
ctx.timerService().deleteEventTimeTimer(oldTimer);
<span class="kw">long</span> newTimer <span class="op">=</span> e.ts <span class="op">+</span> GAP;
ctx.timerService().registerEventTimeTimer(newTimer);
timerTs.update(newTimer);
}
<span class="an">@Override</span>
<span class="kw">public void</span> <span class="fn">onTimer</span>(<span class="kw">long</span> ts, <span class="ty">OnTimerContext</span> ctx, <span class="ty">Collector</span><<span class="ty">Session</span>> out) {
<span class="cm">// Timer fires → 30min inactivity → emit session</span>
out.collect(<span class="kw">new</span> <span class="ty">Session</span>(
ctx.getCurrentKey(),
sessionStart.value(), lastEventTs.value(),
eventCount.value()
));
<span class="cm">// Clear state — this key starts fresh</span>
sessionStart.clear(); lastEventTs.clear();
eventCount.clear(); timerTs.clear();
}
}</pre>
</div>
<div>
<div class="callout c-gold">
<strong>Event-Time Timers — The Superpower</strong>
Timers fire based on the <em>watermark</em>, not the system clock. This means: if you replay 6 months of historical data, timers fire at the correct business times — not 6 months later. Your session detection logic works identically on live data and historical replay. Kafka Streams does not have this capability.
</div>
<h3>RichFunction — Async External Lookups</h3>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">AsyncFunction — non-blocking enrichment</span><span class="code-lang">java</span></div>
<pre><span class="cm">// Never use blocking DB calls in processElement()!
// They block the entire slot thread.
// Use AsyncFunction for external enrichment.</span>
<span class="kw">public class</span> <span class="ty">CustomerEnricher</span>
<span class="kw">extends</span> <span class="ty">RichAsyncFunction</span><<span class="ty">Order</span>, <span class="ty">EnrichedOrder</span>> {
<span class="kw">private transient</span> <span class="ty">AsyncHttpClient</span> client;
<span class="an">@Override</span>
<span class="kw">public void</span> <span class="fn">open</span>(<span class="ty">Configuration</span> params) {
client <span class="op">=</span> <span class="ty">Dsl</span>.asyncHttpClient(); <span class="cm">// non-blocking HTTP</span>
}
<span class="an">@Override</span>
<span class="kw">public void</span> <span class="fn">asyncInvoke</span>(<span class="ty">Order</span> order, <span class="ty">ResultFuture</span><<span class="ty">EnrichedOrder</span>> rf) {
client.prepareGet(<span class="st">"/customers/"</span> <span class="op">+</span> order.customerId)
.execute(<span class="kw">new</span> <span class="ty">AsyncCompletionHandler</span><>() {
<span class="kw">public</span> <span class="ty">Response</span> <span class="fn">onCompleted</span>(<span class="ty">Response</span> resp) {
rf.complete(Collections.singletonList(
<span class="kw">new</span> <span class="ty">EnrichedOrder</span>(order, parseCustomer(resp))
));
<span class="kw">return</span> resp;
}
});
}
}
<span class="cm">// Wire it into the pipeline:</span>
<span class="ty">DataStream</span><<span class="ty">EnrichedOrder</span>> enriched <span class="op">=</span>
<span class="ty">AsyncDataStream</span>.unorderedWait(
orders,
<span class="kw">new</span> <span class="ty">CustomerEnricher</span>(),
<span class="nu">1000</span>, <span class="ty">TimeUnit</span>.MILLISECONDS, <span class="cm">// timeout</span>
<span class="nu">100</span> <span class="cm">// max concurrent requests</span>
);</pre>
</div>
</div>
</div>
</div>
<!-- SIDE OUTPUTS -->
<div id="ds-sideout" class="tab-pane">
<div class="g2">
<div class="code-wrap">
<div class="code-header"><span class="code-fname">Side outputs — multi-stream routing</span><span class="code-lang">java</span></div>
<pre><span class="cm">/**
* Side outputs split a stream into multiple tagged sub-streams
* based on record content — without forking or filtering.
* Zero overhead: routing decision made inline, no copy.
*/</span>
<span class="ty">OutputTag</span><<span class="ty">Order</span>> largeOrders <span class="op">=</span> <span class="kw">new</span> <span class="ty">OutputTag</span><>(<span class="st">"large-orders"</span>){};
<span class="ty">OutputTag</span><<span class="ty">Order</span>> lateEvents <span class="op">=</span> <span class="kw">new</span> <span class="ty">OutputTag</span><>(<span class="st">"late-events"</span>){};
<span class="ty">OutputTag</span><<span class="ty">String</span>> parseErrors <span class="op">=</span> <span class="kw">new</span> <span class="ty">OutputTag</span><>(<span class="st">"parse-errors"</span>){};
<span class="ty">SingleOutputStreamOperator</span><<span class="ty">Order</span>> mainStream <span class="op">=</span> rawInput
.process(<span class="kw">new</span> <span class="ty">ProcessFunction</span><<span class="ty">String</span>, <span class="ty">Order</span>>() {
<span class="kw">public void</span> <span class="fn">processElement</span>(<span class="ty">String</span> raw, <span class="ty">Context</span> ctx,
<span class="ty">Collector</span><<span class="ty">Order</span>> out) {
<span class="kw">try</span> {
<span class="ty">Order</span> o <span class="op">=</span> parse(raw);
<span class="kw">if</span> (o.getAmount() <span class="op">></span> <span class="nu">10_000</span>)
ctx.output(largeOrders, o); <span class="cm">// → fraud review</span>
<span class="kw">if</span> (ctx.timestamp() <span class="op"><</span> ctx.timerService().currentWatermark())
ctx.output(lateEvents, o); <span class="cm">// → late data audit</span>
out.collect(o); <span class="cm">// always goes to main stream too</span>
} <span class="kw">catch</span> (<span class="ty">ParseException</span> e) {
ctx.output(parseErrors, raw); <span class="cm">// → DLQ topic</span>
}
}
});
<span class="cm">// Each is a fully independent DataStream:</span>
<span class="ty">DataStream</span><<span class="ty">Order</span>> fraudReview <span class="op">=</span> mainStream.getSideOutput(largeOrders);
<span class="ty">DataStream</span><<span class="ty">Order</span>> lateAudit <span class="op">=</span> mainStream.getSideOutput(lateEvents);
<span class="ty">DataStream</span><<span class="ty">String</span>> deadLetters <span class="op">=</span> mainStream.getSideOutput(parseErrors);
<span class="cm">// Route to different Kafka topics:</span>
fraudReview.sinkTo(fraudTopic);
lateAudit.sinkTo(auditTopic);
deadLetters.sinkTo(dlqTopic);</pre>
</div>
<div>
<div class="callout c-amber">
<strong>Side Outputs Replace Dead-Letter Queues</strong>
Instead of try/catch-then-produce-to-DLQ in your consumer (which breaks EOS), use Flink side outputs. Parse errors, late records, fraud candidates, and audit events all route to separate physical streams while maintaining exactly-once guarantees end-to-end. The DLQ is no longer an afterthought.
</div>
<h3>Connected Streams — Joining Two Types</h3>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">connect() — heterogeneous stream merge</span><span class="code-lang">java</span></div>
<pre><span class="cm">// connect() joins TWO DIFFERENT TYPES in one operator.
// Unlike union() (same type) or join() (keyed window),
// connect() lets each stream handle its own logic with shared state.</span>
<span class="ty">DataStream</span><<span class="ty">Order</span>> orders <span class="op">=</span> ...;
<span class="ty">DataStream</span><<span class="ty">Config</span>> configs <span class="op">=</span> ...; <span class="cm">// dynamic config changes</span>
<span class="ty">BroadcastStream</span><<span class="ty">Config</span>> bcConfig <span class="op">=</span>
configs.broadcast(configStateDescriptor);
orders.connect(bcConfig)
.process(<span class="kw">new</span> <span class="ty">BroadcastProcessFunction</span><<span class="ty">Order</span>, <span class="ty">Config</span>, <span class="ty">Result</span>>() {
<span class="kw">public void</span> <span class="fn">processElement</span>(<span class="ty">Order</span> order, <span class="ty">ReadOnlyContext</span> ctx, ...) {
<span class="ty">Config</span> cfg <span class="op">=</span> ctx.getBroadcastState(configStateDescriptor)
.get(<span class="st">"current"</span>);
<span class="cm">// Apply latest config to every order — no restart needed</span>
out.collect(applyConfig(order, cfg));
}
<span class="kw">public void</span> <span class="fn">processBroadcastElement</span>(<span class="ty">Config</span> cfg, ...) {
ctx.getBroadcastState(configStateDescriptor).put(<span class="st">"current"</span>, cfg);
}
});</pre>
</div>
</div>
</div>
</div>
</section>
<!-- ══════════════════════════════════════════
SECTION 3 — WATERMARKS
══════════════════════════════════════════ -->
<section id="watermarks">
<div class="section-header">
<div class="section-eyebrow">Time & Ordering</div>
<div class="section-title">WATERMARKS — <em>EVENT TIME</em></div>
<div class="section-sub">Watermarks are Flink's most powerful and most misunderstood concept. They are the mechanism by which a distributed streaming engine reasons about time when data can arrive late, out-of-order, and from multiple parallel partitions simultaneously.</div>
</div>
<div class="section-rule"></div>
<div class="g2">
<div>
<h3>The Three Time Domains</h3>
<table>
<thead><tr><th>Domain</th><th>Clock Source</th><th>Replays Correctly?</th><th>Use For</th></tr></thead>
<tbody>
<tr><td>Event Time</td><td>Timestamp in the record itself</td><td><span style="color:var(--teal)">Yes ✓</span></td><td>All business-critical windows</td></tr>
<tr><td>Processing Time</td><td>System wall clock on TaskManager</td><td><span style="color:var(--crim3)">No ✗</span></td><td>Approximate monitoring only</td></tr>
<tr><td>Ingestion Time</td><td>Timestamp assigned at source</td><td><span style="color:var(--amber)">Partial</span></td><td>Backpressure detection</td></tr>
</tbody>
</table>