Part 1: Apache Iceberg Fundamentals and Table Format - The Beginning of Modern Data Lakehouse
📚 Apache iceberg complete guide 시리즈
Part 2
⏱️ 45 min
📊 Intermediate
Part 1: Apache Iceberg Fundamentals and Table Format - The Beginning of Modern Data Lakehouse
Learn the complete fundamentals of modern data lakehouse from Apache Iceberg’s core concepts to table format, schema evolution, and partitioning strategies.
📋 Table of Contents
- Apache Iceberg Introduction
- Iceberg Architecture and Core Concepts
- Table Format and Metadata Structure
- Schema Evolution and Compatibility
- Partitioning and Sorting Strategies
- Transactions and ACID Guarantees
- Practical Project: Building Iceberg Data Lake
- Learning Summary
🧊 Apache Iceberg Introduction
What is Iceberg?
Apache Iceberg is an open-source table format for modern data lakehouse. It provides a high-performance table format that can efficiently store and manage large-scale data.
Evolution of Data Lakes
| Generation | Characteristics | Key Technologies | Limitations |
|---|---|---|---|
| 1st Generation | File-based Storage | HDFS, S3 | • No Schema Evolution • No ACID Transactions • Limited Metadata |
| 2nd Generation | Table Format Emergence | Hive, HBase | • Limited Schema Evolution • Partial ACID Support • Performance Issues |
| 3rd Generation | Modern Table Formats | Iceberg, Delta Lake, Hudi | • Complete Schema Evolution • ACID Transactions • High Performance Queries |
Core Values of Iceberg
| Value | Description | Benefits |
|---|---|---|
| ACID Transactions | Atomicity, Consistency, Isolation, Durability | • Data Consistency • Concurrency Control • Safe Updates |
| Schema Evolution | Backward Compatibility during Schema Changes | • Flexible Data Model • Gradual Schema Changes • Version Management |
| Time Travel | Query Data from Past Points | • Data Recovery • Experimentation & Analysis • Audit Trail |
| High Performance | Optimized Query Performance | • Fast Scans • Efficient Metadata • Parallel Processing |
🏗️ Iceberg Architecture and Core Concepts
Iceberg Architecture Overview
Iceberg manages large-scale data efficiently through a hierarchical metadata structure.
Core Components
| Component | Role | Features |
|---|---|---|
| Catalog | Table Metadata Management | • Track Table Location • Version Management • Namespace Management |
| Metadata Layer | Table Schema and Partition Info | • JSON/PB Format • Versioned Snapshots • Schema Evolution Support |
| Data Layer | Actual Data Files | • Parquet/ORC/Avro Format • Partition-based Storage • Compression & Encoding |
Metadata Hierarchical Structure
class IcebergMetadataStructure:
def __init__(self):
self.metadata_layers = {}
def explain_metadata_structure(self):
"""Explain Iceberg metadata structure"""
structure = {
"catalog": {
"purpose": "Entry point for table metadata",
"components": [
"Table identifier",
"Current metadata file location",
"Table properties"
],
"examples": ["HiveCatalog", "HadoopCatalog", "JDBC Catalog"]
},
"metadata_file": {
"purpose": "Define table schema and current state",
"components": [
"Table schema (JSON)",
"Current snapshot ID",
"Partition spec",
"Properties and settings"
],
"format": "JSON or Protocol Buffers"
},
"manifest_list": {
"purpose": "List of manifest files for snapshot",
"components": [
"Manifest file paths",
"Partition ranges",
"Statistics information",
"Schema ID"
],
"benefits": [
"Fast snapshot creation",
"Efficient partition pruning",
"Parallel manifest processing"
]
},
"manifest_file": {
"purpose": "Metadata of data files",
"components": [
"Data file paths",
"Partition data",
"File statistics (row count, size, etc.)",
"Column statistics"
],
"benefits": [
"File-level pruning",
"Column-level statistics",
"Efficient scanning"
]
},
"data_files": {
"purpose": "Actual data storage",
"formats": ["Parquet", "ORC", "Avro"],
"features": [
"Column-wise compression",
"Indexing",
"Embedded statistics"
]
}
}
return structure
def demonstrate_metadata_evolution(self):
"""Demonstrate metadata evolution process"""
evolution_process = {
"step_1": {
"action": "Initial table creation",
"metadata_created": [
"metadata.json (v1)",
"manifest-list-1.avro",
"manifest-1.avro"
],
"snapshot_id": "snapshot-1"
},
"step_2": {
"action": "Data insertion",
"metadata_created": [
"manifest-list-2.avro",
"manifest-2.avro (new files)"
],
"snapshot_id": "snapshot-2",
"parent_snapshot": "snapshot-1"
},
"step_3": {
"action": "Schema evolution",
"metadata_created": [
"metadata.json (v2) - new schema",
"manifest-list-3.avro"
],
"snapshot_id": "snapshot-3",
"schema_evolution": "column addition"
},
"step_4": {
"action": "Data deletion/update",
"metadata_created": [
"manifest-list-4.avro",
"delete-files.avro (deletion markers)"
],
"snapshot_id": "snapshot-4",
"operation": "copy-on-write"
}
}
return evolution_process
Iceberg vs Traditional Table Formats
| Feature | Iceberg | Hive | Delta Lake | Apache Hudi |
|---|---|---|---|---|
| ACID Transactions | ✅ Full Support | ❌ Limited | ✅ Full Support | ✅ Full Support |
| Schema Evolution | ✅ Full Support | ❌ Limited | ✅ Full Support | ✅ Full Support |
| Time Travel | ✅ Supported | ❌ Not Supported | ✅ Supported | ✅ Supported |
| Partition Evolution | ✅ Supported | ❌ Not Supported | ❌ Not Supported | ❌ Not Supported |
| Commit Performance | 🟢 Fast | 🔴 Slow | 🟡 Medium | 🟡 Medium |
| Query Performance | 🟢 Optimized | 🔴 Basic | 🟡 Medium | 🟡 Medium |
| Ecosystem Support | 🟢 Extensive | 🟢 Extensive | 🟡 Spark-focused | 🟡 Limited |
📊 Table Format and Metadata Structure
Detailed Table Format Analysis
Iceberg tables provide efficient data management through a hierarchical metadata structure.
Metadata File Structure
class IcebergTableFormat:
def __init__(self):
self.table_structure = {}
def analyze_table_format(self):
"""Detailed table format analysis"""
format_analysis = {
"metadata_hierarchy": {
"level_1": {
"name": "Catalog",
"purpose": "Table location and basic information",
"content": {
"table_identifier": "database.table_name",
"current_metadata_location": "/path/to/metadata.json",
"table_type": "ICEBERG",
"properties": {
"write.format.default": "parquet",
"write.metadata.delete-after-commit.enabled": "true"
}
}
},
"level_2": {
"name": "Metadata File",
"purpose": "Table schema and settings",
"content": {
"format-version": 2,
"table-uuid": "uuid-string",
"location": "/table/path",
"last-updated-ms": 1234567890000,
"last-column-id": 5,
"schema": {
"type": "struct",
"schema-id": 0,
"fields": [
{
"id": 1,
"name": "id",
"required": True,
"type": "long"
},
{
"id": 2,
"name": "name",
"required": False,
"type": "string"
}
]
},
"current-schema-id": 0,
"schemas": [...],
"partition-spec": [...],
"default-spec-id": 0,
"partition-specs": [...],
"last-partition-id": 1000,
"default-sort-order-id": 0,
"sort-orders": [...],
"current-snapshot-id": 123456789,
"refs": {
"main": 123456789
},
"snapshots": [...],
"snapshot-log": [...],
"metadata-log": [...]
}
},
"level_3": {
"name": "Manifest List",
"purpose": "List of manifest files for snapshot",
"content": {
"manifest_path": "/path/to/manifest-list.avro",
"manifest_length": 1024,
"partition_spec_id": 0,
"added_snapshot_id": 123456789,
"added_data_files_count": 100,
"existing_data_files_count": 0,
"deleted_data_files_count": 0,
"partitions": [
{
"contains_null": False,
"lower_bound": "2023-01-01",
"upper_bound": "2023-01-31"
}
],
"added_rows_count": 1000000,
"existing_rows_count": 0,
"deleted_rows_count": 0
}
},
"level_4": {
"name": "Manifest File",
"purpose": "Data file metadata",
"content": {
"manifest_path": "/path/to/manifest.avro",
"manifest_length": 2048,
"partition_spec_id": 0,
"content": 0, # DATA=0, DELETES=1
"sequence_number": 1,
"min_sequence_number": 1,
"snapshot_id": 123456789,
"added_files": [
{
"content": 0,
"file_path": "/data/file1.parquet",
"file_format": "PARQUET",
"partition": {
"year": 2023,
"month": 1
},
"record_count": 100000,
"file_size_in_bytes": 1024000,
"column_sizes": {...},
"value_counts": {...},
"null_value_counts": {...},
"lower_bounds": {...},
"upper_bounds": {...}
}
]
}
}
}
}
return format_analysis
def demonstrate_file_lifecycle(self):
"""Demonstrate file lifecycle"""
lifecycle = {
"create_table": {
"step": 1,
"action": "Table creation",
"files_created": [
"metadata.json",
"manifest-list-1.avro (empty list)",
"manifest-1.avro (empty manifest)"
],
"snapshot": "snapshot-1 (empty snapshot)"
},
"insert_data": {
"step": 2,
"action": "Data insertion",
"files_created": [
"data/file-1.parquet",
"data/file-2.parquet",
"manifest-list-2.avro (including new manifest)",
"manifest-2.avro (new files)"
],
"snapshot": "snapshot-2 (with new data)",
"operation": "append"
},
"update_data": {
"step": 3,
"action": "Data update",
"files_created": [
"data/file-3.parquet (new version)",
"delete/delete-1.parquet (deletion marker)",
"manifest-list-3.avro",
"manifest-3.avro"
],
"snapshot": "snapshot-3 (updated data)",
"operation": "copy-on-write"
},
"compact_data": {
"step": 4,
"action": "Data compaction",
"files_created": [
"data/compacted-file-1.parquet (consolidated file)",
"manifest-list-4.avro",
"manifest-4.avro"
],
"files_removed": [
"data/file-1.parquet",
"data/file-2.parquet",
"delete/delete-1.parquet"
],
"snapshot": "snapshot-4 (compacted data)",
"operation": "rewrite"
}
}
return lifecycle
Metadata Optimization Strategies
| Optimization Area | Strategy | Implementation | Effect |
|---|---|---|---|
| Manifest Size | • Manifest Splitting • Compression Optimization |
• Limit rows per file • gzip/snappy compression |
• Query performance improvement • Metadata size reduction |
| Scan Optimization | • Partition Pruning • Column Pruning |
• Partition range information • Column statistics |
• I/O reduction • Query speed improvement |
| Commit Performance | • Parallel Manifest Generation • Asynchronous Processing |
• Multi-threaded processing • Background tasks |
• Write performance improvement • Latency reduction |
🔄 Schema Evolution and Compatibility
Schema Evolution Concepts
Iceberg ensures backward compatibility during schema changes, allowing safe processing of both existing and new data.
Schema Evolution Rules
| Change Type | Compatibility | Description | Example |
|---|---|---|---|
| Add Column | ✅ Backward Compatible | New column must be nullable | ALTER TABLE ADD COLUMN email STRING |
| Drop Column | ✅ Backward Compatible | Column removed from existing data | ALTER TABLE DROP COLUMN old_field |
| Change Column Type | 🟡 Conditionally Compatible | Only compatible type changes allowed | INT → LONG, STRING → BINARY |
| Change Column Order | ✅ Backward Compatible | Handled by ID-based processing | Column order rearrangement |
| Change Column Requirement | 🟡 Conditionally Compatible | nullable → required not allowed | Only required → nullable allowed |
Schema Evolution Implementation
class SchemaEvolutionManager:
def __init__(self):
self.schema_versions = {}
self.compatibility_rules = {}
def setup_schema_evolution(self):
"""Setup schema evolution"""
evolution_config = {
"schema_evolution_rules": {
"add_column": {
"compatibility": "backward_compatible",
"requirements": [
"New column must be nullable",
"Default value can be specified",
"Column ID is automatically assigned"
],
"example": {
"old_schema": {
"type": "struct",
"fields": [
{"id": 1, "name": "id", "type": "long", "required": True},
{"id": 2, "name": "name", "type": "string", "required": False}
]
},
"new_schema": {
"type": "struct",
"fields": [
{"id": 1, "name": "id", "type": "long", "required": True},
{"id": 2, "name": "name", "type": "string", "required": False},
{"id": 3, "name": "email", "type": "string", "required": False}
]
},
"evolution_type": "add_column"
}
},
"drop_column": {
"compatibility": "backward_compatible",
"requirements": [
"Column is marked as deleted",
"Data is not physically removed",
"Old readers can still access data"
],
"example": {
"old_schema": {
"type": "struct",
"fields": [
{"id": 1, "name": "id", "type": "long", "required": True},
{"id": 2, "name": "name", "type": "string", "required": False},
{"id": 3, "name": "email", "type": "string", "required": False}
]
},
"new_schema": {
"type": "struct",
"fields": [
{"id": 1, "name": "id", "type": "long", "required": True},
{"id": 3, "name": "email", "type": "string", "required": False}
]
},
"evolution_type": "drop_column",
"deleted_columns": [{"id": 2, "name": "name"}]
}
},
"change_column_type": {
"compatibility": "conditional",
"requirements": [
"Type change must be compatible",
"Only widening conversions",
"No data loss allowed"
],
"compatible_changes": [
"int → long",
"float → double",
"string → binary",
"decimal(precision, scale) → decimal(precision+1, scale)"
],
"incompatible_changes": [
"long → int",
"double → float",
"string → int",
"binary → string"
]
}
}
}
return evolution_config
def demonstrate_schema_evolution_process(self):
"""Demonstrate schema evolution process"""
evolution_process = {
"initial_schema": {
"version": 1,
"schema": {
"type": "struct",
"schema-id": 0,
"fields": [
{
"id": 1,
"name": "user_id",
"required": True,
"type": "long"
},
{
"id": 2,
"name": "username",
"required": True,
"type": "string"
},
{
"id": 3,
"name": "created_at",
"required": True,
"type": "timestamp"
}
]
},
"data_files": ["user_data_v1.parquet"]
},
"add_email_column": {
"version": 2,
"schema": {
"type": "struct",
"schema-id": 1,
"fields": [
{
"id": 1,
"name": "user_id",
"required": True,
"type": "long"
},
{
"id": 2,
"name": "username",
"required": True,
"type": "string"
},
{
"id": 3,
"name": "created_at",
"required": True,
"type": "timestamp"
},
{
"id": 4,
"name": "email",
"required": False,
"type": "string"
}
]
},
"data_files": ["user_data_v1.parquet", "user_data_v2.parquet"],
"evolution_type": "add_column"
},
"change_username_to_binary": {
"version": 3,
"schema": {
"type": "struct",
"schema-id": 2,
"fields": [
{
"id": 1,
"name": "user_id",
"required": True,
"type": "long"
},
{
"id": 2,
"name": "username",
"required": True,
"type": "binary"
},
{
"id": 3,
"name": "created_at",
"required": True,
"type": "timestamp"
},
{
"id": 4,
"name": "email",
"required": False,
"type": "string"
}
]
},
"data_files": ["user_data_v1.parquet", "user_data_v2.parquet", "user_data_v3.parquet"],
"evolution_type": "change_column_type"
},
"drop_email_column": {
"version": 4,
"schema": {
"type": "struct",
"schema-id": 3,
"fields": [
{
"id": 1,
"name": "user_id",
"required": True,
"type": "long"
},
{
"id": 2,
"name": "username",
"required": True,
"type": "binary"
},
{
"id": 3,
"name": "created_at",
"required": True,
"type": "timestamp"
}
]
},
"data_files": ["user_data_v1.parquet", "user_data_v2.parquet", "user_data_v3.parquet", "user_data_v4.parquet"],
"evolution_type": "drop_column",
"deleted_columns": [{"id": 4, "name": "email"}]
}
}
return evolution_process
def validate_schema_compatibility(self, old_schema, new_schema):
"""Validate schema compatibility"""
compatibility_check = {
"is_compatible": True,
"issues": [],
"recommendations": []
}
old_fields = {field["id"]: field for field in old_schema["fields"]}
new_fields = {field["id"]: field for field in new_schema["fields"]}
# Check for added fields
added_fields = set(new_fields.keys()) - set(old_fields.keys())
for field_id in added_fields:
field = new_fields[field_id]
if field.get("required", False):
compatibility_check["issues"].append(
f"Added field '{field['name']}' (id: {field_id}) must be nullable"
)
compatibility_check["is_compatible"] = False
# Check for removed fields
removed_fields = set(old_fields.keys()) - set(new_fields.keys())
if removed_fields:
compatibility_check["recommendations"].append(
"Consider marking fields as deprecated before removing them"
)
# Check for type changes
common_fields = set(old_fields.keys()) & set(new_fields.keys())
for field_id in common_fields:
old_field = old_fields[field_id]
new_field = new_fields[field_id]
if old_field["type"] != new_field["type"]:
if not self._is_type_compatible(old_field["type"], new_field["type"]):
compatibility_check["issues"].append(
f"Field '{old_field['name']}' type change from {old_field['type']} to {new_field['type']} is not compatible"
)
compatibility_check["is_compatible"] = False
return compatibility_check
def _is_type_compatible(self, old_type, new_type):
"""Check type compatibility"""
compatible_changes = {
"int": ["long"],
"float": ["double"],
"string": ["binary"],
"decimal(10,2)": ["decimal(11,2)", "decimal(10,3)"]
}
return new_type in compatible_changes.get(old_type, [])
Schema Evolution Best Practices
| Best Practice | Description | Implementation |
|---|---|---|
| Gradual Evolution | Change one at a time | • Step-by-step schema changes • Test at each step |
| Backward Compatibility | Ensure existing data access | • Add nullable columns • Only allow type expansion |
| Version Management | Track schema versions | • Schema ID management • Change history recording |
| Testing Strategy | Validate after evolution | • Compatibility testing • Performance testing |
📁 Partitioning and Sorting Strategies
Partitioning Concepts
Iceberg partitioning logically divides data to improve query performance and management efficiency.
Partitioning Strategies
| Strategy | Description | Advantages | Disadvantages | Use Cases |
|---|---|---|---|---|
| Identity Partitioning | Use column values directly as partitions | • Simplicity • Fast pruning |
• Limited partition count • Difficult schema changes |
• Date-based data • Region-based data |
| Bucket Partitioning | Hash-based partitioning | • Even distribution • Join optimization |
• Inefficient range queries • Fixed partition count |
• Large tables • Join performance critical |
| Truncate Partitioning | String truncation | • String range partitioning • Flexible size |
• Uneven distribution • Uneven partition sizes |
• User IDs • Product codes |
| Year/Month/Day | Time-based hierarchical partitioning | • Time range query optimization • Natural partitioning |
• Increasing partition count • Management complexity |
• Time series data • Log data |
Partitioning Implementation
class IcebergPartitioningStrategy:
def __init__(self):
self.partition_specs = {}
def design_partitioning_strategy(self, table_requirements):
"""Design partitioning strategy"""
strategy = {
"time_series_table": {
"partition_spec": {
"spec_id": 0,
"fields": [
{
"source_id": 4, # created_at column ID
"field_id": 1000,
"name": "created_date",
"transform": "day"
},
{
"source_id": 1, # user_id column ID
"field_id": 1001,
"name": "user_bucket",
"transform": "bucket[16]"
}
]
},
"benefits": [
"Time range query optimization",
"User-based data distribution",
"Parallel processing improvement"
],
"use_case": "User activity logs"
},
"large_analytics_table": {
"partition_spec": {
"spec_id": 0,
"fields": [
{
"source_id": 2, # region column ID
"field_id": 1000,
"name": "region",
"transform": "identity"
},
{
"source_id": 3, # category column ID
"field_id": 1001,
"name": "category",
"transform": "identity"
}
]
},
"benefits": [
"Region-based data separation",
"Category-based query optimization",
"Data locality guarantee"
],
"use_case": "E-commerce order data"
},
"high_frequency_table": {
"partition_spec": {
"spec_id": 0,
"fields": [
{
"source_id": 1, # id column ID
"field_id": 1000,
"name": "id_bucket",
"transform": "bucket[32]"
}
]
},
"benefits": [
"Even data distribution",
"High concurrency support",
"Partition size control"
],
"use_case": "Real-time event data"
}
}
return strategy
def demonstrate_partition_evolution(self):
"""Demonstrate partition evolution"""
evolution_process = {
"initial_partition": {
"spec_id": 0,
"partition_spec": {
"fields": [
{
"source_id": 4,
"field_id": 1000,
"name": "year",
"transform": "year"
}
]
},
"data_layout": "/table/data/year=2023/file1.parquet",
"benefits": ["Year-based query optimization"]
},
"add_month_partition": {
"spec_id": 1,
"partition_spec": {
"fields": [
{
"source_id": 4,
"field_id": 1000,
"name": "year",
"transform": "year"
},
{
"source_id": 4,
"field_id": 1001,
"name": "month",
"transform": "month"
}
]
},
"data_layout": "/table/data/year=2023/month=01/file1.parquet",
"benefits": ["Monthly detailed query optimization"],
"evolution_type": "add_partition_field"
},
"change_to_day_partition": {
"spec_id": 2,
"partition_spec": {
"fields": [
{
"source_id": 4,
"field_id": 1000,
"name": "date",
"transform": "day"
}
]
},
"data_layout": "/table/data/date=2023-01-15/file1.parquet",
"benefits": ["Daily detailed query optimization"],
"evolution_type": "replace_partition_field"
}
}
return evolution_process
Sorting Strategies
| Sorting Strategy | Description | Advantages | Use Cases |
|---|---|---|---|
| Default Sorting | Data insertion order | • Simplicity • Fast insertion |
• General tables |
| Column Sorting | Sort by specific columns | • Range query optimization • Improved compression |
• Time-ordered data • ID-ordered data |
| Composite Sorting | Multi-column sorting | • Complex query optimization • Advanced pruning |
• Multi-dimensional queries • Analytics tables |
⚡ Transactions and ACID Guarantees
ACID Transaction Concepts
Iceberg guarantees ACID (Atomicity, Consistency, Isolation, Durability) properties to provide data consistency and reliability.
Transaction Properties
| Property | Description | Iceberg Implementation | Benefits |
|---|---|---|---|
| Atomicity | Transaction succeeds all or fails all | • Snapshot-based commits • Atomic metadata updates |
• Prevent partial failures • Ensure data consistency |
| Consistency | Database in valid state after transaction | • Schema validation • Constraint checking |
• Prevent invalid data • Business rule compliance |
| Isolation | Prevent interference between concurrent transactions | • Optimistic concurrency control • Snapshot isolation |
• Improve concurrency • Prevent deadlocks |
| Durability | Committed transactions are permanently preserved | • Persistent metadata storage • Replica management |
• Prevent data loss • Disaster recovery support |
Transaction Implementation
class IcebergTransactionManager:
def __init__(self):
self.active_transactions = {}
self.snapshot_manager = SnapshotManager()
def demonstrate_transaction_lifecycle(self):
"""Demonstrate transaction lifecycle"""
transaction_lifecycle = {
"begin_transaction": {
"step": 1,
"action": "Begin transaction",
"process": [
"Generate new snapshot ID",
"Set current snapshot as parent",
"Create transaction context"
],
"metadata": {
"transaction_id": "txn-12345",
"parent_snapshot_id": 100,
"start_time": "2023-01-01T10:00:00Z",
"status": "ACTIVE"
}
},
"modify_data": {
"step": 2,
"action": "Modify data",
"process": [
"Create new data files",
"Mark files for deletion",
"Update manifests"
],
"operations": [
"INSERT: 1000 rows → new_file1.parquet",
"UPDATE: 500 rows → new_file2.parquet",
"DELETE: 200 rows → delete_file1.parquet"
],
"metadata": {
"added_files": 2,
"deleted_files": 1,
"modified_rows": 1500
}
},
"validate_changes": {
"step": 3,
"action": "Validate changes",
"process": [
"Schema consistency check",
"Partition rule validation",
"Constraint verification"
],
"validation_checks": [
"Schema compatibility: ✅ PASS",
"Partition spec: ✅ PASS",
"Data quality: ✅ PASS"
]
},
"commit_transaction": {
"step": 4,
"action": "Commit transaction",
"process": [
"Update metadata file",
"Create new snapshot",
"Update manifest list"
],
"metadata": {
"snapshot_id": 101,
"commit_time": "2023-01-01T10:05:00Z",
"operation": "append",
"summary": {
"added-records": 1000,
"deleted-records": 200,
"total-records": 9800
}
}
},
"rollback_transaction": {
"step": "rollback",
"action": "Rollback transaction",
"process": [
"Delete temporary files",
"Clean up transaction context",
"Maintain parent snapshot"
],
"result": "All changes cancelled, original state restored"
}
}
return transaction_lifecycle
def demonstrate_concurrent_transactions(self):
"""Demonstrate concurrent transaction processing"""
concurrent_scenario = {
"initial_state": {
"snapshot_id": 100,
"table_state": "stable",
"pending_transactions": 0
},
"transaction_a": {
"transaction_id": "txn-A",
"start_time": "10:00:00",
"operation": "INSERT 1000 rows",
"parent_snapshot": 100,
"status": "IN_PROGRESS"
},
"transaction_b": {
"transaction_id": "txn-B",
"start_time": "10:01:00",
"operation": "UPDATE 500 rows",
"parent_snapshot": 100,
"status": "IN_PROGRESS"
},
"commit_sequence": {
"txn_a_commit": {
"time": "10:02:00",
"action": "Transaction A commits",
"new_snapshot": 101,
"result": "Success - 1000 rows added"
},
"txn_b_commit": {
"time": "10:03:00",
"action": "Transaction B attempts commit",
"conflict_check": "Parent snapshot changed (100 → 101)",
"resolution": "Rebase and retry",
"new_snapshot": 102,
"result": "Success - 500 rows updated"
}
},
"final_state": {
"snapshot_id": 102,
"table_state": "stable",
"total_rows": 10500,
"committed_transactions": 2
}
}
return concurrent_scenario
Concurrency Control Mechanisms
| Mechanism | Description | Advantages | Disadvantages |
|---|---|---|---|
| Optimistic Concurrency Control | Assume low conflict and proceed | • High concurrency • No deadlocks |
• Retry needed on conflicts • Complex conflict resolution |
| Snapshot Isolation | Provide consistent view from transaction start | • Improved read performance • Consistent reads |
• Increased memory usage • Complex snapshot management |
| Conflict Detection | Check and resolve conflicts at commit | • Data consistency guarantee • Automatic conflict resolution |
• Retry overhead • Performance impact |
🚀 Practical Project: Building Iceberg Data Lake
Project Overview
Build an Iceberg-based data lakehouse for a large-scale e-commerce platform.
System Architecture
| Component | Technology Stack | Capacity | Role |
|---|---|---|---|
| Storage | S3, HDFS | • 100TB+ data • 1B+ records |
• Permanent data storage • Version management |
| Metadata | Hive Metastore, AWS Glue | • 1000+ tables • 100+ databases |
• Schema management • Table metadata |
| Query Engine | Spark, Presto/Trino | • 100+ concurrent queries • 1TB+/sec processing |
• SQL query execution • Analytics jobs |
| Data Ingestion | Kafka, Flink | • 1M+ events/sec • Real-time streams |
• Data collection • Real-time processing |
Project Implementation
class IcebergDataLakeProject:
def __init__(self):
self.project_config = {}
self.table_manager = IcebergTableManager()
self.schema_manager = SchemaManager()
def design_data_lake_architecture(self):
"""Design data lake architecture"""
architecture = {
"data_domains": {
"user_analytics": {
"tables": [
"user_profiles",
"user_behavior_events",
"user_segments"
],
"partition_strategy": "date + user_bucket",
"retention_policy": "7_years",
"access_patterns": [
"Time range-based analysis",
"User behavior analysis",
"Segment-based aggregation"
]
},
"order_analytics": {
"tables": [
"orders",
"order_items",
"order_events"
],
"partition_strategy": "date + region",
"retention_policy": "10_years",
"access_patterns": [
"Daily sales analysis",
"Regional order analysis",
"Product performance analysis"
]
},
"product_catalog": {
"tables": [
"products",
"categories",
"inventory"
],
"partition_strategy": "category",
"retention_policy": "permanent",
"access_patterns": [
"Product search",
"Category-based analysis",
"Inventory management"
]
}
},
"table_specifications": {
"user_profiles": {
"schema": {
"user_id": "BIGINT",
"username": "STRING",
"email": "STRING",
"created_at": "TIMESTAMP",
"updated_at": "TIMESTAMP",
"profile_data": "STRUCT"
},
"partitioning": [
{"field": "created_at", "transform": "day"},
{"field": "user_id", "transform": "bucket[16]"}
],
"sorting": ["created_at", "user_id"],
"properties": {
"write.format.default": "parquet",
"write.parquet.compression-codec": "snappy"
}
},
"user_behavior_events": {
"schema": {
"event_id": "STRING",
"user_id": "BIGINT",
"event_type": "STRING",
"event_data": "STRUCT",
"timestamp": "TIMESTAMP",
"session_id": "STRING"
},
"partitioning": [
{"field": "timestamp", "transform": "hour"},
{"field": "event_type", "transform": "identity"}
],
"sorting": ["timestamp", "user_id"],
"properties": {
"write.format.default": "parquet",
"write.parquet.compression-codec": "gzip"
}
}
}
}
return architecture
def implement_data_pipeline(self):
"""Implement data pipeline"""
pipeline = {
"real_time_ingestion": {
"source": "Kafka topics",
"processing": "Apache Flink",
"destination": "Iceberg tables",
"flow": [
"Kafka → Flink → Iceberg (user_events)",
"Kafka → Flink → Iceberg (order_events)",
"Kafka → Flink → Iceberg (product_events)"
],
"latency_target": "< 5 minutes"
},
"batch_processing": {
"source": "External systems",
"processing": "Apache Spark",
"destination": "Iceberg tables",
"jobs": [
"Daily user profile updates",
"Weekly order aggregations",
"Monthly product analytics"
],
"schedule": "Daily at 2 AM"
},
"data_quality": {
"validation_rules": [
"Schema validation",
"Data completeness checks",
"Referential integrity",
"Business rule validation"
],
"monitoring": [
"Data freshness metrics",
"Quality score tracking",
"Anomaly detection"
]
}
}
return pipeline
def setup_operational_procedures(self):
"""Setup operational procedures"""
procedures = {
"schema_evolution": {
"process": [
"1. Review schema change request",
"2. Validate compatibility",
"3. Test in staging environment",
"4. Apply to production",
"5. Monitor and validate"
],
"approval_required": "Data architect approval",
"rollback_plan": "Restore to previous schema"
},
"data_retention": {
"policies": {
"user_events": "7 years retention then delete",
"order_data": "10 years retention then archive",
"product_catalog": "Permanent retention"
},
"automation": "Scheduled cleanup jobs",
"compliance": "GDPR, CCPA compliance"
},
"performance_optimization": {
"compaction": {
"frequency": "Weekly",
"criteria": "File count > 100 or size > 1GB",
"strategy": "Automatic compaction"
},
"partition_pruning": {
"monitoring": "Query execution plan analysis",
"optimization": "Partition strategy adjustment"
}
}
}
return procedures
📚 Learning Summary
What We Learned in This Part
- Apache Iceberg Introduction
- Iceberg’s core concepts and values
- Evolution of data lakes
- Comparison with existing table formats
- Iceberg Architecture and Core Concepts
- Hierarchical metadata structure
- Core components and roles
- Metadata evolution process
- Table Format and Metadata Structure
- 4-level metadata hierarchy
- File lifecycle management
- Metadata optimization strategies
- Schema Evolution and Compatibility
- Schema evolution rules and constraints
- Compatibility validation methods
- Evolution process demonstration
- Partitioning and Sorting Strategies
- Various partitioning strategies
- Partition evolution features
- Sorting strategies and optimization
- Transactions and ACID Guarantees
- ACID property implementation
- Transaction lifecycle
- Concurrency control mechanisms
- Practical Project
- Large-scale e-commerce data lake design
- Data pipeline implementation
- Operational procedure setup
Core Technology Stack
| Technology | Role | Importance | Learning Points |
|---|---|---|---|
| Apache Iceberg | Table Format | ⭐⭐⭐⭐⭐ | Metadata structure, ACID guarantees |
| Partitioning | Data Division | ⭐⭐⭐⭐ | Performance optimization, management efficiency |
| Schema Evolution | Flexible Data Model | ⭐⭐⭐⭐⭐ | Compatibility, version management |
| Transactions | Data Consistency | ⭐⭐⭐⭐⭐ | ACID properties, concurrency control |
| Metadata | Table Management | ⭐⭐⭐⭐ | Hierarchical structure, optimization |
Next Part Preview
Part 2: Apache Iceberg Advanced Features and Performance Optimization will cover:
- Advanced partitioning strategies (Evolution, Hidden Partitioning)
- Compaction and cleanup operations
- Query performance optimization
- Metadata management and version control
Series Progress: Apache Iceberg Complete Guide Series
Master Apache Iceberg, the core of modern data lakehouse! 🧊