OLAP Template

olap.yml is optimized for online analytical processing (OLAP). Designed for 4-128 core CPUs with support for large queries, high parallelism, relaxed timeouts, and aggressive vacuum.

Pair with node_tune = olap for OS-level tuning.

Use Cases

OLAP template is ideal for:

• Data warehouses: Historical data storage, multidimensional analysis
• BI reports: Complex report queries, dashboard data sources
• ETL processing: Data extraction, transformation, loading
• Data analysis: Ad-hoc queries, data exploration
• HTAP mixed workloads: Analytical replicas

Workload characteristics:

• Complex queries (seconds to minutes)
• Low concurrent connections (tens to hundreds)
• Read-intensive, writes typically batch operations
• Throughput-sensitive, tolerates higher latency
• Scans large data volumes

Usage

Specify pg_conf = olap.yml in cluster definition:

pg-olap:
  hosts:
    10.10.10.11: { pg_seq: 1, pg_role: primary }
    10.10.10.12: { pg_seq: 2, pg_role: replica }
  vars:
    pg_cluster: pg-olap
    pg_conf: olap.yml    # PostgreSQL analytics template
    node_tune: olap      # OS analytics tuning

Use olap.yml template for dedicated offline replicas:

pg-mixed:
  hosts:
    10.10.10.11: { pg_seq: 1, pg_role: primary }
    10.10.10.12: { pg_seq: 2, pg_role: replica }
    10.10.10.13: { pg_seq: 3, pg_role: offline, pg_conf: olap.yml }  # offline analytics replica
  vars:
    pg_cluster: pg-mixed
    pg_conf: oltp.yml    # primary and online replicas use OLTP
    node_tune: oltp      # OS OLTP tuning

Parameter Details

Connection Management

max_connections: 500
superuser_reserved_connections: 10

OLAP scenarios typically don’t need many connections; 500 is sufficient for most analytical workloads.

Memory Config

OLAP template uses more aggressive memory allocation:

work_mem calculation (differs from OLTP):

work_mem = min(max(shared_buffers / max_connections, 64MB), 8GB)

Larger work_mem allows bigger sort and hash operations in memory, avoiding disk spill.

Locks & Transactions

max_locks_per_transaction: 2-4x maxconn   # OLTP: 1-2x

OLAP queries may involve more tables (partitions, many JOINs), requiring more lock slots.

Parallel Query

OLAP template aggressively enables parallel queries:

max_worker_processes: cpu + 12 (min 20)      # OLTP: cpu + 8
max_parallel_workers: 80% × cpu (min 2)      # OLTP: 50%
max_parallel_workers_per_gather: 50% × cpu   # OLTP: 20% (max 8)
max_parallel_maintenance_workers: 33% × cpu

Parallel cost estimates use defaults to favor parallel plans:

# parallel_setup_cost: 1000    # default, not doubled
# parallel_tuple_cost: 0.1     # default, not doubled

Partition-wise optimization enabled:

enable_partitionwise_join: on       # smart partition JOIN
enable_partitionwise_aggregate: on  # smart partition aggregation

IO Config (PG17+)

io_workers: 50% × cpu (4-32)    # OLTP: 25% (4-16)

More IO workers support parallel large table scans.

WAL Config

min_wal_size: disk/20 (max 200GB)
max_wal_size: disk/5 (max 2000GB)
max_slot_wal_keep_size: disk×3/10 (max 3000GB)
temp_file_limit: disk/5 (max 2000GB)   # OLTP: disk/20

Larger temp_file_limit allows bigger intermediate results to spill to disk.

Vacuum Config

OLAP template uses aggressive vacuum settings:

vacuum_cost_delay: 10ms         # OLTP: 20ms, faster vacuum
vacuum_cost_limit: 10000        # OLTP: 2000, more work per round
autovacuum_max_workers: 3
autovacuum_naptime: 1min
autovacuum_vacuum_scale_factor: 0.08
autovacuum_analyze_scale_factor: 0.04

Analytical databases often have bulk writes requiring aggressive vacuum to reclaim space.

Query Optimization

random_page_cost: 1.1
effective_io_concurrency: 200
default_statistics_target: 1000    # OLTP: 400, more precise stats

Higher default_statistics_target provides more accurate query plans, crucial for complex analytics.

Logging & Monitoring

log_min_duration_statement: 1000    # OLTP: 100ms, relaxed threshold
log_statement: ddl
log_checkpoints: on
log_lock_waits: on
log_temp_files: 1024
log_autovacuum_min_duration: 1s
track_io_timing: on
track_cost_delay_timing: on         # PG18+, track vacuum cost delay
track_functions: all
track_activity_query_size: 8192

Client Timeouts

deadlock_timeout: 50ms
idle_in_transaction_session_timeout: 0   # OLTP: 10min, disabled

Analytical queries may need to hold transactions for extended periods, so idle timeout is disabled.

Key Differences from OLTP

Performance Tuning Tips

With TimescaleDB

OLAP template works great with TimescaleDB:

pg-timeseries:
  vars:
    pg_conf: olap.yml
    pg_libs: 'timescaledb, pg_stat_statements, auto_explain'
    pg_extensions:
      - timescaledb

With pg_duckdb

For ultimate analytical performance, combine with pg_duckdb:

pg-analytics:
  vars:
    pg_conf: olap.yml
    pg_libs: 'pg_duckdb, pg_stat_statements, auto_explain'

Columnar Storage

Consider columnar storage extensions:

pg_extensions:
  - citus_columnar  # or pg_mooncake

Resource Isolation

For mixed workloads, isolate analytics to dedicated replicas:

pg-mixed:
  hosts:
    10.10.10.11: { pg_seq: 1, pg_role: primary }               # OLTP writes
    10.10.10.12: { pg_seq: 2, pg_role: replica }               # OLTP reads
    10.10.10.13: { pg_seq: 3, pg_role: offline }               # OLAP analytics
  vars:
    pg_cluster: pg-mixed

Monitoring Metrics

Focus on these metrics:

• Query time: Long query execution time distribution
• Parallelism: Parallel worker utilization
• Temp files: Temp file size and count
• Disk IO: Sequential and index scan IO volume
• Cache hit ratio: shared_buffers and OS cache hit rates

References

• pg_conf: PostgreSQL config template selection
• node_tune: OS tuning template, should match pg_conf
• OLTP Template: Transaction template comparison
• CRIT Template: Critical business template comparison
• TINY Template: Micro instance template comparison
• Offline Replica: Dedicated analytics instances