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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 Iceberg — Production Field Guide</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@300;400;500;700&family=JetBrains+Mono:wght@400;500;700&family=Syne:wght@700;800&display=swap" rel="stylesheet">
<style>
:root {
--bg: #06090f;
--bg1: #090e18;
--bg2: #0d1422;
--bg3: #111c2e;
--surface: #172135;
--border: #1a2a42;
--border2: #1f3454;
--ice: #7ef0ff;
--ice2: #3ac8e0;
--ice3: #1a7a90;
--ice4: #0a3a48;
--amber: #ffc040;
--amber2: #b88000;
--red: #ff5060;
--red2: #b02030;
--green: #40e890;
--green2: #1a9a58;
--purple: #a080ff;
--purple2: #6040cc;
--coral: #ff7060;
--text: #c0d8e8;
--text2: #6a9ab0;
--text3: #3a6070;
--dim: #1a2a3a;
}
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
html { scroll-behavior: smooth; }
body {
background: var(--bg);
color: var(--text);
font-family: 'Space Grotesk', sans-serif;
font-size: 15px;
line-height: 1.6;
min-height: 100vh;
}
/* subtle grid texture */
body::after {
content: '';
position: fixed; inset: 0; z-index: 9999; pointer-events: none;
background-image:
linear-gradient(rgba(126,240,255,0.012) 1px, transparent 1px),
linear-gradient(90deg, rgba(126,240,255,0.012) 1px, transparent 1px);
background-size: 40px 40px;
}
/* ── HEADER ──────────────────────────────────────────────── */
header {
border-bottom: 1px solid var(--border2);
padding: 24px 48px 20px;
position: sticky; top: 0; z-index: 100;
background: rgba(6,9,15,0.97);
backdrop-filter: blur(12px);
display: flex; align-items: flex-end; justify-content: space-between;
}
.brand { display: flex; align-items: flex-end; gap: 20px; }
.logo-mark {
font-family: 'Syne', sans-serif;
font-size: 48px; font-weight: 800; line-height: 1;
color: var(--ice);
letter-spacing: -3px;
}
.brand-text { padding-bottom: 4px; }
.brand-name {
font-family: 'Syne', sans-serif;
font-size: 22px; font-weight: 800;
color: #ddf0f8; letter-spacing: -0.5px;
}
.brand-sub {
font-family: 'JetBrains Mono', monospace;
font-size: 11px; letter-spacing: 3px; color: var(--text3);
text-transform: uppercase; margin-top: 2px;
}
.header-right {
font-family: 'JetBrains Mono', monospace;
font-size: 11px; color: var(--text3);
text-align: right; line-height: 2;
}
.badge {
display: inline-block;
background: rgba(126,240,255,0.1);
border: 1px solid rgba(126,240,255,0.25);
color: var(--ice2);
padding: 2px 10px; border-radius: 20px;
font-size: 11px; font-weight: 500;
}
/* ── TABS ────────────────────────────────────────────────── */
.tab-bar {
display: flex;
background: var(--bg1);
border-bottom: 1px solid var(--border2);
padding: 0 48px;
overflow-x: auto; gap: 0;
position: sticky; top: 89px; z-index: 99;
}
.tab-bar::-webkit-scrollbar { height: 2px; }
.tab-bar::-webkit-scrollbar-thumb { background: var(--border2); }
.tab-btn {
font-family: 'JetBrains Mono', monospace;
font-size: 11px; font-weight: 700; letter-spacing: 2px;
text-transform: uppercase; color: var(--text3);
background: none; border: none; cursor: pointer;
padding: 13px 18px;
border-bottom: 2px solid transparent;
transition: all .18s; white-space: nowrap;
}
.tab-btn:hover { color: var(--text); }
.tab-btn.active { color: var(--ice); border-bottom-color: var(--ice); }
/* ── MAIN ────────────────────────────────────────────────── */
main { max-width: 1300px; margin: 0 auto; padding: 0 48px 100px; }
.tab-content { display: none; padding-top: 52px; }
.tab-content.active { display: block; animation: fadein .2s ease; }
@keyframes fadein { from {opacity:0;transform:translateY(6px)} to {opacity:1;transform:translateY(0)} }
/* ── SECTION TITLES ──────────────────────────────────────── */
.sect-label {
font-family: 'JetBrains Mono', monospace;
font-size: 10px; font-weight: 700; letter-spacing: 5px;
color: var(--ice3); text-transform: uppercase; margin-bottom: 10px;
}
h2.sect-title {
font-family: 'Syne', sans-serif;
font-size: 40px; font-weight: 800; letter-spacing: -1px;
color: #d8eef8; line-height: 1.05; margin-bottom: 14px;
}
h3.sub-title {
font-family: 'Syne', sans-serif;
font-size: 20px; font-weight: 700;
color: var(--ice2); margin: 44px 0 12px;
display: flex; align-items: center; gap: 12px;
}
h3.sub-title::before {
content: ''; flex-shrink: 0;
width: 28px; height: 2px;
background: linear-gradient(90deg, var(--ice), transparent);
}
h4 {
font-family: 'Space Grotesk', sans-serif;
font-size: 15px; font-weight: 700; letter-spacing: 0.3px;
color: var(--amber); margin: 28px 0 8px;
}
p { color: var(--text2); margin-bottom: 12px; font-weight: 300; line-height: 1.78; }
strong { color: var(--text); font-weight: 500; }
code { font-family: 'JetBrains Mono', monospace; font-size: 12px; color: var(--ice); background: var(--ice4); padding: 1px 7px; border-radius: 3px; }
a { color: var(--ice2); text-decoration: none; border-bottom: 1px solid var(--border2); }
a:hover { border-bottom-color: var(--ice); }
/* ── CODE BLOCKS ─────────────────────────────────────────── */
.code-block {
background: var(--bg1);
border: 1px solid var(--border2);
border-left: 3px solid var(--ice3);
border-radius: 6px;
margin: 14px 0 26px;
overflow: hidden;
}
.code-block.warn-block { border-left-color: var(--amber2); }
.code-block.crit-block { border-left-color: var(--red2); }
.code-header {
display: flex; align-items: center; justify-content: space-between;
padding: 8px 16px;
background: var(--bg2);
border-bottom: 1px solid var(--border);
font-family: 'JetBrains Mono', monospace; font-size: 11px;
}
.code-lang { color: var(--ice3); font-weight: 700; letter-spacing: 2px; text-transform: uppercase; }
.code-file { color: var(--text3); }
pre {
padding: 20px 24px;
overflow-x: auto;
font-family: 'JetBrains Mono', monospace;
font-size: 12.5px; line-height: 1.78;
color: var(--text);
tab-size: 4;
}
pre::-webkit-scrollbar { height: 4px; }
pre::-webkit-scrollbar-track { background: transparent; }
pre::-webkit-scrollbar-thumb { background: var(--border2); border-radius: 2px; }
/* Syntax */
.k { color: var(--purple); }
.s { color: var(--amber); }
.c { color: var(--text3); font-style: italic; }
.f { color: var(--ice); }
.n { color: var(--green); }
.d { color: #88aaff; }
.t { color: #ff9988; }
.bi { color: #88bbff; }
.op { color: var(--ice2); }
.cm { color: var(--text3); }
/* ── CALLOUTS ────────────────────────────────────────────── */
.callout {
padding: 14px 20px; border-radius: 5px;
margin: 14px 0; border-left: 3px solid;
font-size: 13.5px; line-height: 1.65;
}
.callout.warn { background: rgba(255,192,64,0.06); border-color: var(--amber); color: #c89028; }
.callout.info { background: rgba(126,240,255,0.05); border-color: var(--ice3); color: var(--ice2); }
.callout.crit { background: rgba(255,80,96,0.06); border-color: var(--red); color: var(--red); }
.callout.tip { background: rgba(64,232,144,0.05); border-color: var(--green2);color: var(--green); }
.callout.purple{ background: rgba(160,128,255,0.06);border-color: var(--purple2);color: var(--purple); }
.callout strong { color: inherit; font-weight: 600; }
/* ── CARDS ───────────────────────────────────────────────── */
.card-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(270px, 1fr)); gap: 14px; margin: 18px 0; }
.card-grid-2 { grid-template-columns: repeat(auto-fit, minmax(380px, 1fr)); }
.card {
background: var(--bg2);
border: 1px solid var(--border2);
border-radius: 8px; padding: 20px;
}
.card-title {
font-family: 'Syne', sans-serif;
font-size: 17px; font-weight: 700;
color: #d8eef8; margin-bottom: 8px;
}
.card p { font-size: 13px; margin: 0; }
.tag {
display: inline-block;
font-family: 'JetBrains Mono', monospace;
font-size: 10px; font-weight: 700; letter-spacing: 1.5px;
text-transform: uppercase; padding: 2px 8px; border-radius: 3px; margin-bottom: 8px;
}
.tag.ice { background: var(--ice4); color: var(--ice); border: 1px solid var(--ice3); }
.tag.amber { background: rgba(255,192,64,0.1); color: var(--amber); border: 1px solid var(--amber2); }
.tag.red { background: rgba(255,80,96,0.1); color: var(--red); border: 1px solid var(--red2); }
.tag.green { background: rgba(64,232,144,0.08); color: var(--green); border: 1px solid var(--green2); }
.tag.purple { background: rgba(160,128,255,0.1); color: var(--purple); border: 1px solid var(--purple2); }
.tag.coral { background: rgba(255,112,96,0.1); color: var(--coral); border: 1px solid rgba(255,112,96,0.3); }
/* ── METRICS ─────────────────────────────────────────────── */
.metric-row { display: flex; gap: 12px; margin: 20px 0; flex-wrap: wrap; }
.metric {
flex: 1; min-width: 130px;
background: var(--bg2); border: 1px solid var(--border2); border-radius: 6px;
padding: 14px 16px;
}
.metric-val {
font-family: 'Syne', sans-serif;
font-size: 30px; font-weight: 800; line-height: 1;
}
.metric-label { font-size: 11px; color: var(--text3); margin-top: 5px; text-transform: uppercase; letter-spacing: 1.5px; font-family: 'JetBrains Mono', monospace; }
/* ── TABLES ──────────────────────────────────────────────── */
.data-table { width: 100%; border-collapse: collapse; margin: 14px 0; font-size: 13px; }
.data-table th {
background: var(--bg2); color: var(--text3);
font-family: 'JetBrains Mono', monospace; font-size: 10px; font-weight: 700;
letter-spacing: 2px; text-transform: uppercase;
padding: 10px 14px; text-align: left; border-bottom: 1px solid var(--border2);
}
.data-table td { padding: 10px 14px; border-bottom: 1px solid var(--border); color: var(--text2); vertical-align: top; }
.data-table tr:hover td { background: var(--bg2); }
.data-table td:first-child { font-family: 'JetBrains Mono', monospace; color: var(--ice2); font-size: 12px; white-space: nowrap; }
.data-table td.ok { color: var(--green); }
.data-table td.warn{ color: var(--amber); }
.data-table td.bad { color: var(--red); }
/* ── ARCHITECTURE LAYERS ─────────────────────────────────── */
.layer-stack { margin: 20px 0; }
.layer {
display: flex; align-items: center; gap: 16px;
padding: 16px 20px; margin-bottom: 3px;
border-radius: 5px; position: relative;
}
.layer-label {
font-family: 'JetBrains Mono', monospace;
font-size: 11px; font-weight: 700; letter-spacing: 2px;
text-transform: uppercase; min-width: 90px;
}
.layer-content { flex: 1; font-size: 13px; color: var(--text2); }
.layer-items {
display: flex; flex-wrap: wrap; gap: 6px;
font-family: 'JetBrains Mono', monospace; font-size: 11px;
}
.layer-item {
background: var(--bg); border: 1px solid var(--border2);
padding: 3px 10px; border-radius: 3px;
}
.layer.l1 { background: rgba(126,240,255,0.06); border: 1px solid rgba(126,240,255,0.15); }
.layer.l1 .layer-label { color: var(--ice); }
.layer.l2 { background: rgba(160,128,255,0.06); border: 1px solid rgba(160,128,255,0.15); }
.layer.l2 .layer-label { color: var(--purple); }
.layer.l3 { background: rgba(64,232,144,0.05); border: 1px solid rgba(64,232,144,0.15); }
.layer.l3 .layer-label { color: var(--green); }
/* ── DECISION MATRIX ─────────────────────────────────────── */
.decision-row {
display: grid; grid-template-columns: 1fr 1fr 1fr;
gap: 12px; margin: 16px 0;
}
.decision-cell {
background: var(--bg2); border: 1px solid var(--border2);
border-radius: 6px; padding: 16px;
}
.decision-cell h5 {
font-family: 'JetBrains Mono', monospace;
font-size: 11px; font-weight: 700; letter-spacing: 2px;
text-transform: uppercase; color: var(--text3); margin-bottom: 10px;
}
.decision-cell ul { list-style: none; }
.decision-cell li {
font-size: 12.5px; color: var(--text2); padding: 4px 0;
border-bottom: 1px solid var(--border); display: flex; gap: 8px;
}
.decision-cell li:last-child { border: none; }
.decision-cell li::before { content: '›'; color: var(--ice3); flex-shrink: 0; }
/* ── CHECKLIST ───────────────────────────────────────────── */
.checklist { list-style: none; margin: 12px 0; }
.checklist li {
padding: 9px 0 9px 30px; border-bottom: 1px solid var(--border);
position: relative; font-size: 13.5px; color: var(--text2);
}
.checklist li::before { content: '▸'; position: absolute; left: 0; color: var(--ice3); font-size: 12px; top: 10px; }
.checklist li strong { color: var(--text); }
/* ── FLOW DIAGRAM ────────────────────────────────────────── */
.flow { display: flex; align-items: center; gap: 6px; flex-wrap: wrap; margin: 16px 0; }
.flow-node {
background: var(--surface); border: 1px solid var(--border2);
border-radius: 5px; padding: 8px 14px;
font-family: 'JetBrains Mono', monospace; font-size: 12px;
color: var(--text2); text-align: center; line-height: 1.4;
}
.flow-node.active { border-color: var(--ice2); color: var(--ice); }
.flow-node.warn { border-color: var(--amber2); color: var(--amber); }
.flow-node.new { border-color: var(--green2); color: var(--green); }
.flow-arrow { color: var(--text3); font-size: 18px; }
/* ── VS COMPARE ──────────────────────────────────────────── */
.vs-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; margin: 16px 0; }
.vs-card { background: var(--bg2); border: 1px solid var(--border2); border-radius: 8px; overflow: hidden; }
.vs-header { padding: 12px 16px; border-bottom: 1px solid var(--border); display: flex; align-items: center; justify-content: space-between; }
.vs-title { font-family: 'Syne', sans-serif; font-size: 16px; font-weight: 700; }
.vs-body { padding: 14px 16px; }
.vs-row { display: flex; justify-content: space-between; padding: 7px 0; border-bottom: 1px solid var(--border); font-size: 12.5px; }
.vs-row:last-child { border: none; }
.vs-key { color: var(--text3); font-family: 'JetBrains Mono', monospace; font-size: 11px; }
.vs-val { color: var(--text2); }
/* ── DIVIDER ─────────────────────────────────────────────── */
.divider { height: 1px; background: var(--border); margin: 44px 0; }
/* ── FOOTER ──────────────────────────────────────────────── */
footer {
margin-top: 80px; padding: 28px 48px;
border-top: 1px solid var(--border);
display: flex; justify-content: space-between; align-items: center;
font-family: 'JetBrains Mono', monospace; font-size: 11px; color: var(--text3);
}
</style>
</head>
<body>
<header>
<div class="brand">
<div class="logo-mark">∆ICE</div>
<div class="brand-text">
<div class="brand-name">Apache Iceberg</div>
<div class="brand-sub">Production Field Guide — Spec v1 / v2 / v3</div>
</div>
</div>
<div class="header-right">
<span class="badge">Format Spec · Not an Engine</span><br>
Spark · Trino · Flink · Hive · DuckDB<br>
Parquet · ORC · Avro · REST Catalog
</div>
</header>
<div class="tab-bar">
<button class="tab-btn active" onclick="switchTab('adopt')">Adoption Guide</button>
<button class="tab-btn" onclick="switchTab('arch')">Architecture</button>
<button class="tab-btn" onclick="switchTab('catalog')">Catalogs</button>
<button class="tab-btn" onclick="switchTab('writes')">Writes & DML</button>
<button class="tab-btn" onclick="switchTab('reads')">Reads & Perf</button>
<button class="tab-btn" onclick="switchTab('maintenance')">Maintenance</button>
<button class="tab-btn" onclick="switchTab('engines')">Engine Setup</button>
<button class="tab-btn" onclick="switchTab('ops')">Ops & Risks</button>
</div>
<main>
<!-- ════════════════════════════════════════════════════════════
TAB: ADOPTION GUIDE
═══════════════════════════════════════════════════════════════ -->
<section class="tab-content active" id="adopt">
<div class="sect-label">// 00 — Before You Commit</div>
<h2 class="sect-title">Should You Adopt Iceberg?</h2>
<p>Iceberg is a <strong>table format specification</strong>, not an engine or a database. It defines the layout of files on object storage and how multiple compute engines can safely read and write them. Adopting it is an infrastructure commitment with genuine operational weight — answer these questions honestly first.</p>
<h3 class="sub-title">Pre-Adoption Decision Framework</h3>
<div class="decision-row">
<div class="decision-cell">
<h5>Adopt Iceberg if...</h5>
<ul>
<li>Multiple engines need the same tables (Spark + Trino + Flink)</li>
<li>You need ACID writes with snapshot isolation</li>
<li>You need time travel / reproducible queries</li>
<li>Schema evolution without ETL rewrites is critical</li>
<li>Tables exceed 1 TB and file pruning matters</li>
<li>CDC / row-level updates / deletes are required</li>
<li>Partition evolution without breaking existing queries</li>
<li>Regulatory need to audit or roll back table state</li>
</ul>
</div>
<div class="decision-cell">
<h5>Maybe don't if...</h5>
<ul>
<li>Single-engine workload (Spark only → Delta might be simpler)</li>
<li>Purely streaming, no batch reads</li>
<li>Tables < 100 GB — plain Parquet is faster to operate</li>
<li>No dedicated data engineers to run compaction</li>
<li>Your cloud platform is already Delta-native (Databricks)</li>
<li>Existing Hive tables work and you have no pain</li>
<li>Append-only logs with no time travel need</li>
</ul>
</div>
<div class="decision-cell">
<h5>Clarify before starting</h5>
<ul>
<li>Row-level updates / deletes — or append-only analytics?</li>
<li>Which engines need write access vs. read-only?</li>
<li>Do you need nanosecond timestamps or geospatial types? (V3)</li>
<li>Catalog: managed (Glue) or self-hosted (Nessie)?</li>
<li>Existing Hive tables to migrate or greenfield?</li>
<li>Who owns compaction scheduling?</li>
<li>Storage backend: S3 / GCS / ADLS?</li>
<li>SLA on stale metadata / snapshot expiry?</li>
</ul>
</div>
</div>
<h3 class="sub-title">Storage Backend Comparison</h3>
<table class="data-table">
<thead><tr><th>Backend</th><th>Strong Consistency?</th><th>Atomic Rename?</th><th>Cost Profile</th><th>Notes</th></tr></thead>
<tbody>
<tr><td>AWS S3</td><td class="ok">Yes (since 2020)</td><td class="bad">No — use DynamoDB lock</td><td>$0.023/GB + req</td><td>Most mature. Use S3 DynamoDB locking for catalog safety with Hive Metastore.</td></tr>
<tr><td>GCS</td><td class="ok">Yes</td><td class="ok">Yes (multi-obj)</td><td>$0.020/GB + ops</td><td>Best atomic semantics outside cloud. BigLake catalog integrates natively.</td></tr>
<tr><td>Azure ADLS Gen2</td><td class="ok">Yes</td><td class="ok">Yes (HNS)</td><td>~$0.018/GB + tx</td><td>Hierarchical namespace required for atomic directory ops. Enable HNS from the start.</td></tr>
<tr><td>MinIO (on-prem)</td><td class="ok">Yes</td><td class="warn">Partial</td><td>Infra cost</td><td>S3-compatible. Works well; needs proper replication setup for durability.</td></tr>
</tbody>
</table>
<h3 class="sub-title">Key Decisions at a Glance</h3>
<div class="card-grid">
<div class="card">
<div class="tag ice">File Format</div>
<div class="card-title">Parquet, ORC, or Avro?</div>
<p><strong>Default: Parquet.</strong> Best columnar read performance for analytics. ORC if primarily Hive-native and you need transactional efficiency there. Avro for streaming write-heavy workloads (row-oriented, fast append) — rarely used for analytical queries. Iceberg V3 adds Variant type for semi-structured JSON embedded in Parquet.</p>
</div>
<div class="card">
<div class="tag purple">Catalog</div>
<div class="card-title">Hive / Glue / Nessie / REST?</div>
<p><strong>Greenfield on AWS:</strong> Glue Data Catalog. <strong>Multi-cloud or Git-like branching:</strong> Nessie. <strong>Vendor-neutral / multi-engine:</strong> REST catalog (Polaris, Unity Catalog OSS). <strong>Existing Hadoop:</strong> Hive Metastore. Catalog choice determines multi-engine safety — never share a table across engines without a catalog enforcing the metadata pointer.</p>
</div>
<div class="card">
<div class="tag amber">Write Pattern</div>
<div class="card-title">Copy-on-Write vs. Merge-on-Read</div>
<p><strong>Copy-on-Write (COW):</strong> rewrites data files on each update — expensive writes, fast reads. Use for OLAP where reads dominate. <strong>Merge-on-Read (MOR):</strong> writes delete files + new data files — cheap writes, reads must merge. Use for CDC / high-frequency updates. Can mix per-table and change later.</p>
</div>
<div class="card">
<div class="tag green">Compaction</div>
<div class="card-title">Who Runs Maintenance?</div>
<p>Small files accumulate fast with streaming writes or MOR. Without compaction, reads degrade. You <strong>must</strong> schedule: <code>rewriteDataFiles</code> (bin-packing), <code>expireSnapshots</code>, and <code>removeOrphanFiles</code>. Options: Spark job (most flexible), Hive scheduled query, or managed services (AWS EMR Serverless, Tabular). This is non-negotiable operational work.</p>
</div>
</div>
<div class="callout warn">
<strong>The hidden complexity:</strong> Iceberg's format is elegant; the <em>operations surrounding it</em> are not. Catalog management, compaction scheduling, snapshot expiry, and cross-engine compatibility all require sustained engineering attention. Iceberg is not a "set it and forget it" solution.
</div>
</section>
<!-- ════════════════════════════════════════════════════════════
TAB: ARCHITECTURE
═══════════════════════════════════════════════════════════════ -->
<section class="tab-content" id="arch">
<div class="sect-label">// 01 — Format Internals</div>
<h2 class="sect-title">The Three-Layer Architecture</h2>
<p>Iceberg is a specification of files on object storage. Every "table" is a set of JSON/Avro metadata files plus the actual data files. Understanding this layering explains every behavioral characteristic of the format.</p>
<div class="layer-stack">
<div class="layer l1">
<div class="layer-label">Catalog</div>
<div class="layer-content">
<div style="font-size:13px;margin-bottom:8px;color:var(--text)">Stores a single pointer: <code>table_name → metadata.json path</code>. Manages atomic swaps of that pointer.</div>
<div class="layer-items">
<span class="layer-item" style="color:var(--ice2)">Hive Metastore</span>
<span class="layer-item" style="color:var(--ice2)">AWS Glue</span>
<span class="layer-item" style="color:var(--ice2)">Nessie</span>
<span class="layer-item" style="color:var(--ice2)">REST Catalog</span>
<span class="layer-item" style="color:var(--ice2)">JDBC Catalog</span>
<span class="layer-item" style="color:var(--ice2)">DynamoDB Catalog</span>
</div>
</div>
</div>
<div style="text-align:center;color:var(--text3);font-size:22px;margin:2px 0">↕</div>
<div class="layer l2">
<div class="layer-label">Metadata</div>
<div class="layer-content">
<div style="font-size:13px;margin-bottom:8px;color:var(--text)">Immutable JSON & Avro files. Snapshot list, schema versions, partition specs, manifests. Small — lives on object storage alongside data.</div>
<div class="layer-items">
<span class="layer-item" style="color:var(--purple)">metadata.json</span>
<span class="layer-item" style="color:var(--purple)">snapshot-N.avro</span>
<span class="layer-item" style="color:var(--purple)">manifest-list.avro</span>
<span class="layer-item" style="color:var(--purple)">manifest-file.avro</span>
</div>
</div>
</div>
<div style="text-align:center;color:var(--text3);font-size:22px;margin:2px 0">↕</div>
<div class="layer l3">
<div class="layer-label">Data</div>
<div class="layer-content">
<div style="font-size:13px;margin-bottom:8px;color:var(--text)">Actual data files and delete files. Named by UUID — never renamed or mutated. Immutable once written.</div>
<div class="layer-items">
<span class="layer-item" style="color:var(--green)">*.parquet</span>
<span class="layer-item" style="color:var(--green)">*.orc</span>
<span class="layer-item" style="color:var(--green)">*.avro</span>
<span class="layer-item" style="color:var(--amber)">positional-delete.avro</span>
<span class="layer-item" style="color:var(--amber)">equality-delete.avro</span>
</div>
</div>
</div>
</div>
<h3 class="sub-title">Snapshot Mechanics — What Happens on Every Write</h3>
<div class="flow">
<div class="flow-node active">Writer<br>writes data file(s)</div>
<div class="flow-arrow">→</div>
<div class="flow-node new">New manifest<br>lists data files</div>
<div class="flow-arrow">→</div>
<div class="flow-node new">New manifest list<br>references manifests</div>
<div class="flow-arrow">→</div>
<div class="flow-node new">New metadata.json<br>adds snapshot</div>
<div class="flow-arrow">→</div>
<div class="flow-node warn">Catalog atomic swap<br>old ptr → new ptr</div>
<div class="flow-arrow">→</div>
<div class="flow-node">Readers see<br>new snapshot</div>
</div>
<p>Old data files and old metadata are never deleted immediately — they remain valid until <code>expireSnapshots</code> runs. This is the source of <strong>time travel</strong> and also the source of <strong>storage bloat</strong> if expiry is not scheduled.</p>
<div class="code-block">
<div class="code-header"><span class="code-lang">python</span><span class="code-file">arch/inspect_metadata.py — understand your table internals</span></div>
<pre><span class="c">"""
Inspect a live Iceberg table's metadata without modifying it.
Uses PyIceberg — the Python library for the Iceberg spec.
pip install pyiceberg[s3,glue]
"""</span>
<span class="k">from</span> pyiceberg.catalog.glue <span class="k">import</span> GlueCatalog
<span class="k">from</span> pyiceberg.catalog <span class="k">import</span> load_catalog
<span class="k">import</span> json, datetime
<span class="c"># ── Connect to catalog ──────────────────────────────────────</span>
catalog = <span class="f">GlueCatalog</span>(
name=<span class="s">"prod"</span>,
**{
<span class="s">"type"</span>: <span class="s">"glue"</span>,
<span class="s">"s3.region"</span>: <span class="s">"us-east-1"</span>,
<span class="s">"s3.access-key-id"</span>: <span class="s">"..."</span>, <span class="c"># prefer instance role / env</span>
<span class="s">"s3.secret-access-key"</span>: <span class="s">"..."</span>,
}
)
table = catalog.<span class="f">load_table</span>(<span class="s">"analytics.events"</span>)
<span class="c"># ── 1. Schema and Partition Spec ────────────────────────────</span>
<span class="f">print</span>(<span class="s">"=== SCHEMA ==="</span>)
<span class="k">for</span> field <span class="k">in</span> table.schema().fields:
<span class="f">print</span>(<span class="f">f" [{field.field_id:>3}] {field.name:<30} {str(field.field_type):<20} required={field.required}"</span>)
<span class="f">print</span>(<span class="s">"\n=== PARTITION SPEC ==="</span>)
<span class="k">for</span> pf <span class="k">in</span> table.spec().fields:
<span class="f">print</span>(<span class="f">f" {pf.name} = {pf.transform}({pf.source_id})"</span>)
<span class="c"># ── 2. Snapshot History ─────────────────────────────────────</span>
<span class="f">print</span>(<span class="s">"\n=== LAST 10 SNAPSHOTS ==="</span>)
snapshots = table.metadata.snapshots[-<span class="n">10</span>:]
<span class="k">for</span> snap <span class="k">in</span> snapshots:
ts = datetime.datetime.<span class="f">fromtimestamp</span>(snap.timestamp_ms / <span class="n">1000</span>, tz=datetime.timezone.utc)
<span class="f">print</span>(
<span class="f">f" snapshot_id={snap.snapshot_id} "
f"ts={ts.isoformat()} "
f"op={snap.summary.get('operation','?'):<12} "
f"added_files={snap.summary.get('added-data-files','0'):<6} "
f"deleted_files={snap.summary.get('deleted-data-files','0')}"</span>
)
<span class="c"># ── 3. Current manifest statistics ─────────────────────────</span>
<span class="f">print</span>(<span class="s">"\n=== MANIFEST STATS (current snapshot) ==="</span>)
current = table.<span class="f">current_snapshot</span>()
<span class="k">if</span> current:
summary = current.summary
total_recs = <span class="f">int</span>(summary.get(<span class="s">'total-records'</span>, <span class="n">0</span>))
total_files = <span class="f">int</span>(summary.get(<span class="s">'total-data-files'</span>, <span class="n">0</span>))
total_size = <span class="f">int</span>(summary.get(<span class="s">'total-files-size'</span>, <span class="n">0</span>))
del_files = <span class="f">int</span>(summary.get(<span class="s">'total-delete-files'</span>, <span class="n">0</span>))
<span class="f">print</span>(<span class="f">f" Records: {total_recs:>15,}"</span>)
<span class="f">print</span>(<span class="f">f" Data files: {total_files:>15,}"</span>)
<span class="f">print</span>(<span class="f">f" Delete files: {del_files:>15,}"</span>)
<span class="f">print</span>(<span class="f">f" Total size: {total_size / 1_073_741_824:>14.2f} GiB"</span>)
avg_size = total_size / total_files <span class="k">if</span> total_files > <span class="n">0</span> <span class="k">else</span> <span class="n">0</span>
<span class="f">print</span>(<span class="f">f" Avg file sz: {avg_size / 1_048_576:>14.1f} MiB {'⚠ small files!' if avg_size < 64*1024*1024 else '✓ healthy'}"</span>)
<span class="c"># ── 4. Time travel scan ─────────────────────────────────────</span>
<span class="f">print</span>(<span class="s">"\n=== TIME TRAVEL READ (as of 24h ago) ==="</span>)
<span class="k">import</span> pyarrow <span class="k">as</span> pa
yesterday_ms = <span class="f">int</span>((datetime.datetime.<span class="f">now</span>() - datetime.timedelta(days=<span class="n">1</span>)).timestamp() * <span class="n">1000</span>)
historical = table.<span class="f">scan</span>(snapshot_id=<span class="k">None</span>, as_of_timestamp=yesterday_ms).<span class="f">to_arrow</span>()
<span class="f">print</span>(<span class="f">f" Row count 24h ago: {len(historical):,}"</span>)
<span class="f">print</span>(<span class="f">f" Schema: {historical.schema}"</span>)</pre>
</div>
<h3 class="sub-title">Iceberg V1 vs V2 vs V3 — What Changed</h3>
<table class="data-table">
<thead><tr><th>Feature</th><th>V1</th><th>V2</th><th>V3</th></tr></thead>
<tbody>
<tr><td>Row-level deletes</td><td class="bad">No</td><td class="ok">Yes (positional + equality)</td><td class="ok">Yes + deletion vectors</td></tr>
<tr><td>Row lineage</td><td class="bad">No</td><td class="ok">sequence numbers</td><td class="ok">Yes</td></tr>
<tr><td>Nanosecond timestamps</td><td class="bad">No</td><td class="bad">No</td><td class="ok">Yes (timestamptz_ns)</td></tr>
<tr><td>Variant type (semi-structured)</td><td class="bad">No</td><td class="bad">No</td><td class="ok">Yes</td></tr>
<tr><td>Geospatial types</td><td class="bad">No</td><td class="bad">No</td><td class="ok">geometry / geography</td></tr>
<tr><td>Default sort order in metadata</td><td class="bad">No</td><td class="ok">Yes</td><td class="ok">Yes</td></tr>
<tr><td>Multiple partition specs</td><td class="bad">No</td><td class="ok">Yes</td><td class="ok">Yes</td></tr>
</tbody>
</table>
<div class="callout warn">
<strong>V3 adoption:</strong> As of 2024–2025, V3 is spec-complete but engine support is uneven. Spark 3.5+ and Trino 430+ have partial V3 support. Verify your engine supports the V3 features you need before specifying <code>'format-version' = '3'</code> at table creation — a V2 engine will refuse to read a V3 table.
</div>
</section>
<!-- ════════════════════════════════════════════════════════════
TAB: CATALOGS
═══════════════════════════════════════════════════════════════ -->
<section class="tab-content" id="catalog">
<div class="sect-label">// 02 — Metadata Coordination</div>
<h2 class="sect-title">Catalog Deep Dive</h2>
<p>The catalog is the <strong>single most critical architectural decision</strong> in an Iceberg deployment. It enforces the atomic metadata pointer swap that makes ACID writes possible. A misconfigured catalog leads to silent data corruption — two writers can both commit and one overwrites the other.</p>
<div class="vs-grid">
<div class="vs-card">
<div class="vs-header" style="border-color:var(--border)">
<span class="vs-title" style="color:var(--ice2)">AWS Glue Data Catalog</span>
<span class="tag ice">Managed</span>
</div>
<div class="vs-body">
<div class="vs-row"><span class="vs-key">Concurrency control</span><span class="vs-val" style="color:var(--green)">Optimistic locking via Glue API</span></div>
<div class="vs-row"><span class="vs-key">Engine support</span><span class="vs-val">Spark, Trino, Flink, Athena, EMR</span></div>
<div class="vs-row"><span class="vs-key">Branching/tagging</span><span class="vs-val" style="color:var(--red)">No</span></div>
<div class="vs-row"><span class="vs-key">Multi-cloud</span><span class="vs-val" style="color:var(--red)">AWS only</span></div>
<div class="vs-row"><span class="vs-key">Best for</span><span class="vs-val">AWS-native stacks, serverless Athena queries</span></div>
<div class="vs-row"><span class="vs-key">Gotcha</span><span class="vs-val" style="color:var(--amber)">10MB metadata size limit per table object</span></div>
</div>
</div>
<div class="vs-card">
<div class="vs-header" style="border-color:var(--border)">
<span class="vs-title" style="color:var(--purple)">Nessie</span>
<span class="tag purple">Git-for-Data</span>
</div>
<div class="vs-body">
<div class="vs-row"><span class="vs-key">Concurrency control</span><span class="vs-val" style="color:var(--green)">Optimistic + MVCC commits</span></div>
<div class="vs-row"><span class="vs-key">Engine support</span><span class="vs-val">Spark, Flink, Trino (via REST adapter)</span></div>
<div class="vs-row"><span class="vs-key">Branching/tagging</span><span class="vs-val" style="color:var(--green)">Yes — full Git-like branching of tables</span></div>
<div class="vs-row"><span class="vs-key">Multi-cloud</span><span class="vs-val" style="color:var(--green)">Yes — vendor neutral</span></div>
<div class="vs-row"><span class="vs-key">Best for</span><span class="vs-val">Multi-engine, experimentation, data-as-code</span></div>
<div class="vs-row"><span class="vs-key">Gotcha</span><span class="vs-val" style="color:var(--amber)">Self-hosted; operational burden</span></div>
</div>
</div>
<div class="vs-card">
<div class="vs-header" style="border-color:var(--border)">
<span class="vs-title" style="color:var(--amber)">Hive Metastore (HMS)</span>
<span class="tag amber">Legacy</span>
</div>
<div class="vs-body">
<div class="vs-row"><span class="vs-key">Concurrency control</span><span class="vs-val" style="color:var(--amber)">DB-level locking (fragile)</span></div>
<div class="vs-row"><span class="vs-key">Engine support</span><span class="vs-val">Spark, Trino, Flink, Hive</span></div>
<div class="vs-row"><span class="vs-key">Branching/tagging</span><span class="vs-val" style="color:var(--red)">No</span></div>
<div class="vs-row"><span class="vs-key">Multi-cloud</span><span class="vs-val" style="color:var(--green)">Yes</span></div>
<div class="vs-row"><span class="vs-key">Best for</span><span class="vs-val">Migration from existing Hive deployments</span></div>
<div class="vs-row"><span class="vs-key">Gotcha</span><span class="vs-val" style="color:var(--red)">Concurrent writers can corrupt — must serialize</span></div>
</div>
</div>
<div class="vs-card">
<div class="vs-header" style="border-color:var(--border)">
<span class="vs-title" style="color:var(--green)">REST Catalog (Polaris / Unity OSS)</span>
<span class="tag green">Standard</span>
</div>
<div class="vs-body">
<div class="vs-row"><span class="vs-key">Concurrency control</span><span class="vs-val" style="color:var(--green)">Optimistic, server-side enforcement</span></div>
<div class="vs-row"><span class="vs-key">Engine support</span><span class="vs-val" style="color:var(--green)">Any engine with REST client (all major)</span></div>
<div class="vs-row"><span class="vs-key">Branching/tagging</span><span class="vs-val">Depends on implementation</span></div>
<div class="vs-row"><span class="vs-key">Multi-cloud</span><span class="vs-val" style="color:var(--green)">Yes — the future standard</span></div>
<div class="vs-row"><span class="vs-key">Best for</span><span class="vs-val">Greenfield multi-engine, vendor neutral</span></div>
<div class="vs-row"><span class="vs-key">Gotcha</span><span class="vs-val" style="color:var(--amber)">Polaris OSS is young; Unity is Databricks-tied</span></div>
</div>
</div>
</div>
<h3 class="sub-title">Catalog Configuration — PyIceberg & Spark</h3>
<div class="code-block">
<div class="code-header"><span class="code-lang">python</span><span class="code-file">catalog/pyiceberg_catalog.py</span></div>
<pre><span class="c">"""
PyIceberg catalog initialization patterns for all major catalog types.
pip install "pyiceberg[s3,glue,nessie,sql,pyarrow]"
"""</span>
<span class="k">from</span> pyiceberg.catalog <span class="k">import</span> load_catalog
<span class="c"># ── 1. AWS Glue ─────────────────────────────────────────────</span>
glue_catalog = <span class="f">load_catalog</span>(
<span class="s">"prod_glue"</span>,
**{
<span class="s">"type"</span>: <span class="s">"glue"</span>,
<span class="s">"s3.region"</span>: <span class="s">"us-east-1"</span>,
<span class="c"># Prefer IAM role — omit keys and rely on boto3 credential chain</span>
<span class="s">"s3.endpoint"</span>: <span class="s">"https://s3.us-east-1.amazonaws.com"</span>,
<span class="s">"glue.region"</span>: <span class="s">"us-east-1"</span>,
}
)
<span class="c"># ── 2. Nessie REST ──────────────────────────────────────────</span>
nessie_catalog = <span class="f">load_catalog</span>(
<span class="s">"nessie"</span>,
**{
<span class="s">"type"</span>: <span class="s">"nessie"</span>,
<span class="s">"uri"</span>: <span class="s">"http://nessie.data.internal:19120/api/v2"</span>,
<span class="s">"ref"</span>: <span class="s">"main"</span>, <span class="c"># default branch</span>
<span class="s">"authentication.type"</span>: <span class="s">"BEARER"</span>,
<span class="s">"authentication.token"</span>: <span class="s">"..."</span>, <span class="c"># use Secrets Manager</span>
<span class="s">"s3.region"</span>: <span class="s">"us-east-1"</span>,
<span class="s">"warehouse"</span>: <span class="s">"s3://my-lake/warehouse"</span>,
}
)
<span class="c"># ── 3. REST Catalog (generic — Polaris / Unity OSS) ─────────</span>
rest_catalog = <span class="f">load_catalog</span>(
<span class="s">"polaris"</span>,
**{
<span class="s">"type"</span>: <span class="s">"rest"</span>,
<span class="s">"uri"</span>: <span class="s">"https://polaris.internal/api/catalog"</span>,
<span class="s">"credential"</span>: <span class="s">"client_id:client_secret"</span>,
<span class="s">"warehouse"</span>: <span class="s">"prod_warehouse"</span>,
<span class="s">"scope"</span>: <span class="s">"PRINCIPAL_ROLE:analytics_rw"</span>,
}
)
<span class="c"># ── 4. Local JDBC (dev / CI) ────────────────────────────────</span>
local_catalog = <span class="f">load_catalog</span>(
<span class="s">"local"</span>,
**{
<span class="s">"type"</span>: <span class="s">"sql"</span>,
<span class="s">"uri"</span>: <span class="s">"sqlite:////tmp/iceberg_dev.db"</span>,
<span class="s">"warehouse"</span>: <span class="s">"/tmp/iceberg_warehouse"</span>,
}
)
<span class="c"># ── Namespace & table creation (catalog-agnostic) ────────────</span>
<span class="k">import</span> pyarrow <span class="k">as</span> pa
<span class="k">from</span> pyiceberg.schema <span class="k">import</span> Schema
<span class="k">from</span> pyiceberg.types <span class="k">import</span> (
NestedField, StringType, LongType, TimestamptzType,
DoubleType, BooleanType, IntegerType
)
<span class="k">from</span> pyiceberg.partitioning <span class="k">import</span> PartitionSpec, PartitionField
<span class="k">from</span> pyiceberg.transforms <span class="k">import</span> DayTransform, BucketTransform
catalog = glue_catalog <span class="c"># or whichever you chose</span>
<span class="c"># Create namespace if it doesn't exist</span>
<span class="k">if</span> (<span class="s">"analytics"</span>,) <span class="k">not in</span> catalog.<span class="f">list_namespaces</span>():
catalog.<span class="f">create_namespace</span>(<span class="s">"analytics"</span>, properties={
<span class="s">"owner"</span>: <span class="s">"data-engineering"</span>,
<span class="s">"location"</span>: <span class="s">"s3://my-lake/analytics"</span>,
})
<span class="c"># Define schema with explicit field IDs (important for evolution)</span>
schema = <span class="f">Schema</span>(
<span class="f">NestedField</span>(<span class="n">1</span>, <span class="s">"event_id"</span>, <span class="f">StringType</span>(), required=<span class="k">True</span>),
<span class="f">NestedField</span>(<span class="n">2</span>, <span class="s">"user_id"</span>, <span class="f">LongType</span>(), required=<span class="k">True</span>),
<span class="f">NestedField</span>(<span class="n">3</span>, <span class="s">"event_type"</span>, <span class="f">StringType</span>(), required=<span class="k">True</span>),
<span class="f">NestedField</span>(<span class="n">4</span>, <span class="s">"occurred_at"</span>, <span class="f">TimestamptzType</span>(), required=<span class="k">True</span>),
<span class="f">NestedField</span>(<span class="n">5</span>, <span class="s">"revenue"</span>, <span class="f">DoubleType</span>(), required=<span class="k">False</span>),
<span class="f">NestedField</span>(<span class="n">6</span>, <span class="s">"country"</span>, <span class="f">StringType</span>(), required=<span class="k">False</span>),
<span class="f">NestedField</span>(<span class="n">7</span>, <span class="s">"is_bot"</span>, <span class="f">BooleanType</span>(), required=<span class="k">False</span>),
)
partition_spec = <span class="f">PartitionSpec</span>(
<span class="f">PartitionField</span>(source_id=<span class="n">4</span>, field_id=<span class="n">1000</span>, transform=<span class="f">DayTransform</span>(), name=<span class="s">"day"</span>),
<span class="f">PartitionField</span>(source_id=<span class="n">2</span>, field_id=<span class="n">1001</span>, transform=<span class="f">BucketTransform</span>(<span class="n">16</span>), name=<span class="s">"user_bucket"</span>),
)
table = catalog.<span class="f">create_table</span>(
identifier=<span class="s">"analytics.events"</span>,
schema=schema,
partition_spec=partition_spec,
location=<span class="s">"s3://my-lake/analytics/events"</span>,
properties={
<span class="s">"format-version"</span>: <span class="s">"2"</span>,
<span class="s">"write.format.default"</span>: <span class="s">"parquet"</span>,
<span class="s">"write.parquet.compression-codec"</span>: <span class="s">"zstd"</span>,
<span class="s">"write.target-file-size-bytes"</span>: str(<span class="n">134_217_728</span>), <span class="c"># 128 MiB</span>
<span class="s">"write.delete.format.default"</span>: <span class="s">"parquet"</span>,
<span class="s">"write.merge.mode"</span>: <span class="s">"merge-on-read"</span>, <span class="c"># or copy-on-write</span>
<span class="s">"commit.retry.num-retries"</span>: <span class="s">"4"</span>,
<span class="s">"commit.retry.min-wait-ms"</span>: <span class="s">"100"</span>,
<span class="s">"commit.retry.max-wait-ms"</span>: <span class="s">"60000"</span>,
<span class="s">"history.expire.max-snapshot-age-ms"</span>: str(<span class="n">7</span> * <span class="n">24</span> * <span class="n">3600</span> * <span class="n">1000</span>),
}
)</pre>
</div>
</section>
<!-- ════════════════════════════════════════════════════════════
TAB: WRITES & DML
═══════════════════════════════════════════════════════════════ -->
<section class="tab-content" id="writes">
<div class="sect-label">// 03 — ACID, COW, MOR & Deletes</div>
<h2 class="sect-title">Writes, DML & Concurrency</h2>
<h3 class="sub-title">Copy-on-Write vs. Merge-on-Read — The Full Breakdown</h3>
<div class="card-grid card-grid-2">
<div class="card">
<div class="tag ice">Copy-on-Write (COW)</div>
<div class="card-title">Expensive Writes, Fast Reads</div>
<p>On UPDATE or DELETE, Iceberg reads the affected data files, rewrites them with the changes applied, and produces a new snapshot pointing at the new files. Old files are orphaned (retained until expiry).</p>
<br>
<p><strong>When to use:</strong> OLAP tables where reads vastly outnumber writes. Reporting tables, dimension tables, star schema fact tables with infrequent corrections. Any table where read latency is the primary SLA.</p>
<br>
<p><strong>Cost model:</strong> Each update amplifies write I/O. Updating 1 row in a 1 GiB file rewrites the full 1 GiB. For tables with high-frequency point updates, COW is financially ruinous at scale.</p>
</div>
<div class="card">
<div class="tag amber">Merge-on-Read (MOR)</div>
<div class="card-title">Cheap Writes, Reads Do More Work</div>
<p>On UPDATE or DELETE, Iceberg writes a <strong>delete file</strong> that records which rows to exclude (positional delete = row-file offset pairs; equality delete = key value matches). Data files are NOT rewritten.</p>
<br>
<p><strong>When to use:</strong> CDC pipelines, GDPR delete workflows, frequent small updates to large tables. Streaming ingestion with corrections. Any pattern where writes are frequent and large file rewrites are unacceptable.</p>
<br>
<p><strong>Cost model:</strong> Delete files accumulate. Reads must merge data files with all open delete files. Without compaction, read latency degrades. The <code>delete-file-count</code> metric is your MOR health indicator.</p>
</div>
</div>
<div class="code-block">
<div class="code-header"><span class="code-lang">python</span><span class="code-file">writes/spark_dml.py — COW and MOR patterns in Spark</span></div>
<pre><span class="c">"""
Spark DML with Iceberg — full patterns for INSERT, UPDATE, DELETE, MERGE.
Configure SparkSession with Iceberg extensions before running.
"""</span>
<span class="k">from</span> pyspark.sql <span class="k">import</span> SparkSession
<span class="k">from</span> pyspark.sql <span class="k">import</span> functions <span class="k">as</span> F
<span class="k">from</span> pyspark.sql.types <span class="k">import</span> *
spark = (
SparkSession.<span class="f">builder</span>
.<span class="f">config</span>(<span class="s">"spark.sql.extensions"</span>,
<span class="s">"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions"</span>)
.<span class="f">config</span>(<span class="s">"spark.sql.catalog.prod"</span>,
<span class="s">"org.apache.iceberg.spark.SparkCatalog"</span>)
.<span class="f">config</span>(<span class="s">"spark.sql.catalog.prod.type"</span>, <span class="s">"glue"</span>)
.<span class="f">config</span>(<span class="s">"spark.sql.catalog.prod.warehouse"</span>, <span class="s">"s3://my-lake/warehouse"</span>)
.<span class="f">config</span>(<span class="s">"spark.sql.defaultCatalog"</span>, <span class="s">"prod"</span>)
.<span class="f">getOrCreate</span>()
)
<span class="c"># ── 1. Append (fastest, always correct) ─────────────────────</span>
new_events_df.<span class="f">writeTo</span>(<span class="s">"prod.analytics.events"</span>).<span class="f">append</span>()
<span class="c"># ── 2. Overwrite by partition (idempotent batch) ─────────────</span>
<span class="c"># Replaces only partitions present in the new data — safe for reruns</span>
(
new_events_df
.<span class="f">writeTo</span>(<span class="s">"prod.analytics.events"</span>)
.<span class="f">option</span>(<span class="s">"partitionOverwriteMode"</span>, <span class="s">"dynamic"</span>)
.<span class="f">overwritePartitions</span>()
)
<span class="c"># ── 3. MERGE INTO — the Swiss Army knife ────────────────────</span>
<span class="c"># Register source as temp view</span>
cdc_df.<span class="f">createOrReplaceTempView</span>(<span class="s">"cdc_source"</span>)
spark.<span class="f">sql</span>(<span class="s">"""
MERGE INTO prod.analytics.events AS target
USING (
SELECT
event_id, user_id, event_type, occurred_at,
revenue, country, is_bot,
_op_type -- 'I' insert, 'U' update, 'D' delete
FROM cdc_source
WHERE _op_type IN ('I', 'U', 'D')
) AS source
ON target.event_id = source.event_id
WHEN MATCHED AND source._op_type = 'D' THEN DELETE
WHEN MATCHED AND source._op_type = 'U' THEN UPDATE SET
target.revenue = source.revenue,
target.country = source.country,
target.is_bot = source.is_bot
WHEN NOT MATCHED AND source._op_type != 'D' THEN INSERT *
"""</span>)
<span class="c"># ── 4. DELETE with filter ────────────────────────────────────</span>
<span class="c"># GDPR right-to-erasure — MOR writes a delete file, fast operation</span>
spark.<span class="f">sql</span>(<span class="s">"""
DELETE FROM prod.analytics.events
WHERE user_id IN (SELECT user_id FROM gdpr_deletion_requests WHERE processed_at IS NULL)
"""</span>)
<span class="c"># ── 5. UPDATE ────────────────────────────────────────────────</span>
spark.<span class="f">sql</span>(<span class="s">"""
UPDATE prod.analytics.events
SET is_bot = TRUE
WHERE user_id IN (SELECT user_id FROM bot_detection_list)
AND occurred_at > CURRENT_TIMESTAMP - INTERVAL 30 DAYS
"""</span>)
<span class="c"># ── 6. INSERT OVERWRITE — replace a specific partition ───────</span>
spark.<span class="f">sql</span>(<span class="s">"""
INSERT OVERWRITE prod.analytics.events
PARTITION (day = '2024-01-15')
SELECT * FROM staging.events_corrected
WHERE date(occurred_at) = '2024-01-15'
"""</span>)
<span class="c"># ── 7. Time travel reads ─────────────────────────────────────</span>
<span class="c"># By snapshot ID</span>
df_snap = spark.<span class="f">read</span>.<span class="f">option</span>(<span class="s">"snapshot-id"</span>, <span class="s">"5782341283"</span>).<span class="f">table</span>(<span class="s">"prod.analytics.events"</span>)
<span class="c"># By timestamp</span>
df_ts = spark.<span class="f">read</span>.<span class="f">option</span>(<span class="s">"as-of-timestamp"</span>, <span class="s">"1705276800000"</span>).<span class="f">table</span>(<span class="s">"prod.analytics.events"</span>)
<span class="c"># Incremental read — changes between two snapshots</span>
df_incr = (
spark.<span class="f">read</span>
.<span class="f">format</span>(<span class="s">"iceberg"</span>)
.<span class="f">option</span>(<span class="s">"start-snapshot-id"</span>, <span class="s">"5780000000"</span>)
.<span class="f">option</span>(<span class="s">"end-snapshot-id"</span>, <span class="s">"5782341283"</span>)
.<span class="f">load</span>(<span class="s">"prod.analytics.events"</span>)
)
<span class="c"># ── 8. Schema evolution (no file rewrites!) ──────────────────</span>
spark.<span class="f">sql</span>(<span class="s">"ALTER TABLE prod.analytics.events ADD COLUMN session_id BIGINT"</span>)
spark.<span class="f">sql</span>(<span class="s">"ALTER TABLE prod.analytics.events RENAME COLUMN country TO country_code"</span>)
<span class="c"># Existing files return NULL for new columns — zero cost</span>
<span class="c"># ── 9. Partition evolution (V2+) ─────────────────────────────</span>
<span class="c"># Change partition without rewriting old data</span>
spark.<span class="f">sql</span>(<span class="s">"""
ALTER TABLE prod.analytics.events
ADD PARTITION FIELD hour(occurred_at) -- add hourly on top of daily
"""</span>)
<span class="c"># New data is partitioned by day+hour; old data stays day-only</span>
<span class="c"># Query engine handles both specs transparently</span></pre>
</div>
<h3 class="sub-title">Concurrency — Commit Conflicts & Retry Logic</h3>
<div class="code-block">
<div class="code-header"><span class="code-lang">python</span><span class="code-file">writes/concurrent_writes.py</span></div>
<pre><span class="c">"""
Iceberg uses optimistic concurrency control (OCC):
1. Writer reads current metadata
2. Performs computation
3. Attempts commit (catalog CAS: old_metadata → new_metadata)
4. If another writer committed between steps 1 and 3 → CommitFailedException
5. Retry with exponential backoff
Safe concurrent patterns:
- Multiple appenders: always safe (different files)
- Appender + overwriter: conflict if same partition
- Two overwriters of same partition: one loses, must retry
"""</span>
<span class="k">from</span> pyiceberg.catalog <span class="k">import</span> load_catalog
<span class="k">from</span> pyiceberg.exceptions <span class="k">import</span> CommitFailedException
<span class="k">import</span> pyarrow <span class="k">as</span> pa
<span class="k">import</span> time, random, logging
log = logging.<span class="f">getLogger</span>(__name__)
<span class="k">def</span> <span class="f">write_with_retry</span>(
catalog_name: str,
table_id: str,
data: pa.Table,
overwrite: bool = <span class="k">False</span>,
max_retries: int = <span class="n">5</span>,
base_delay: float = <span class="n">0.5</span>,
) -> <span class="k">None</span>:
<span class="s">"""
Write PyArrow table to Iceberg with OCC retry loop.
For Spark, set spark.sql.iceberg.handle-timestamp-without-timezone=true
and use native Spark retry config instead.
"""</span>