Storage Architecture Fundamentals

In the Greenplum (MPP PostgreSQL) ecosystem, Append-Optimized Row (AORO) and Append-Optimized Column (AOCO) tables represent specialized storage formats designed for analytical workloads. The core distinction lies in their physical organization:

• AORO: Data is stored sequentially by row, optimized for writing full records.
• AOCO: Data is stored sequentially by column, optimized for reading specific fields.

Unlike standard Heap tables (stadnard PostgreSQL tables with MVCC), these Append-Optimized formats do not support in-place updates efficiently and are tailored for high-volume data ingestion.

Physical Layout Comparison

Storage Type	Organization	Update Mechanism
Standard Heap	Row-based	In-place (MVCC)
AORO	Row-based	Append-only (Delete + Insert)
AOCO	Column-based	Append-only (Per Column)

AORO Structure

Complete rows are written contiguously to disk:

[ Record 1 (Col1, Col2, Col3) ]
[ Record 2 (Col1, Col2, Col3) ]
[ Record 3 (Col1, Col2, Col3) ]

AOCO Structure

Each column is stored in its own separate physical file or segment:

Column_1_File: Val1, Val2, Val3, Val4
Column_2_File: Val1, Val2, Val3, Val4
Column_3_File: Val1, Val2, Val3, Val4

Detailed Use Case Analysis

Standard Heap Tables

• Role: Ideal for OLTP operations, dimension tables, and small lookup tables.
• Scale: Best for datasets under 10GB or fewer than 1 million rows.
• Behavior: High-frequency single-row updates/deletes, indexed point lookups, and concurrent small transactions.
• Examples: User dimensions, configuration settings, product catalogs.

AORO (Row-Oriented)

• Role: Storing large volumes of detailed records where full rows are often retrieved.
• Scale: Hundreds of millions to billions of rows.
• Behavior: Batch ETL inserts, minimal updates. Queries often involve SELECT * or retrieving most columns.
• Examples: Application logs, raw event streams, audit trails.
• Note: Adding a column with a default value requires a full table rewrite, which is expensive on large datasets.

AOCO (Column-Oriented)

• Role: The powerhouse for Data Warehousing fact tables and OLAP analytics.
• Scale: Terabyte or Petabyte scale, often with very wide schemas (100+ columns).
• Behavior: Queries touch only a handful of columns (e.g., aggregations on specific metrics). Writes are bulk appends.
• Examples: Financial transaction facts, user behavior analysis, wide feature tables for ML.
• Note: Supports adding nullable columns instantly without rewriting data.

Performance Benchmark Comparison

Metric	Heap	AORO	AOCO
Scan Speed (Selective)	Poor	Moderate	Excellent
Compression Ratio	Low	Medium (Table-level)	High (Column-level)
Ingestion Speed	Slow	Fastest	Fast
Update/Delete Cost	Low	High	Very High
Schema Changes	Slow	Slow (Rewrite)	Instant (Nullable)

Decision Matrix for Workloads

When to choose AORO (Row)

• Full Row Access: You frequently need every column in the row (e.g., exporting raw logs).
• Moderate Width: Tables with fewer than 100 columns.
• Write Heavy: The workload is primarily inserting data with few updates.
• Example Query:

SELECT * FROM server_logs WHERE log_id = 12345;

When to choose AOCO (Column)

• Massive Scale: Tables ranging from Terabytes to Petabytes.
• Wide Tables: Schemas exceeding 100 columns (e.g., user profiles, tags).
• Projected Reads: Queries access only a subset of columns.
• Heavy Aggregation: Frequent use of SUM, COUNT, AVG, GROUP BY.
• Compression Needs: Significant storage savings using ZSTD or RLE encoding.
• Example Query:

SELECT SUM(revenue), AVG(latency) 
FROM network_stats 
WHERE capture_date BETWEEN '2023-01-01' AND '2023-01-31';
-- Only reads 3 columns out of 150

Performance Case Study

Scenario: 1 Billion rows, 120 columns, querying 3 columns.

Storage Type	Query Latency
Heap	120s
AORO	95s
AOCO	6s

Creation Syntax Examples

Creating an AORO Table

CREATE TABLE raw_metrics (
    event_time timestamp,
    source_ip inet,
    payload text
) 
WITH (appendonly=true, orientation=row);

Creating an AOCO Table

CREATE TABLE sales_fact (
    transaction_id bigint,
    amount numeric,
    region text
) 
WITH (appendonly=true, orientation=column);

Creating AOCO with Compression

CREATE TABLE user_analytics (
    user_id bigint,
    session_count int,
    last_active date
) 
WITH (
    appendonly=true, 
    orientation=column, 
    compresstype=zstd, 
    compresslevel=7
);

Analyzing Database Workload Patterns

To determine if a table is read-heavy or write-heavy, query the statistics from pg_stat_user_tables.

SELECT
    schemaname,
    relname AS table_name,
    seq_scan + idx_scan AS total_reads,
    n_tup_ins + n_tup_upd + n_tup_del AS total_writes,
    pg_size_pretty(pg_total_relation_size(relid)) AS size
FROM pg_stat_user_tables
ORDER BY total_reads DESC;

Automated Workload Classification

SELECT
    schemaname,
    relname,
    CASE
        WHEN (seq_scan + idx_scan) > 10 * (n_tup_ins + n_tup_upd + n_tup_del) 
            THEN 'Read Intensive'
        WHEN (n_tup_ins + n_tup_upd + n_tup_del) > (seq_scan + idx_scan) 
            THEN 'Write Intensive'
        ELSE 'Mixed Workload'
    END AS workload_profile
FROM pg_stat_user_tables;

Storage Selection Strategy

Workload Profile	Recommended Storage
Read Intensive (Analytics)	AOCO
Write Intensive (Logs)	AORO
Frequent Updates	Heap
Wide Table Aggregation	AOCO