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In software engineering, requirements drive design. The previous comparison showed what differs between OLTP and OLAP systems. This page examines why—the specific requirements from users, business operations, regulatory constraints, and technical realities that make these differences necessary.
Understanding requirements goes beyond feature lists. It means grasping:
This deep understanding enables architects to make principled trade-offs rather than cargo-culting patterns from other organizations. Your requirements may differ from Google's or Netflix's—your architecture should reflect your constraints, not theirs.
By the end of this page, you will understand the specific requirements categories that differentiate OLTP and OLAP systems: latency and throughput requirements, consistency and durability requirements, availability and recovery requirements, user and application requirements, and regulatory and compliance requirements. You'll see how these requirements cascade into architectural decisions.
Latency requirements differ by orders of magnitude between OLTP and OLAP, and these differences are rooted in how humans and systems interact with data:
OLTP: Interactive Response Requirements
OLTP transactions are part of interactive workflows. A user clicks a button and expects immediate feedback. Research consistently shows:
For automated systems, latency requirements are even stricter. A payment gateway timeout might be 5 seconds—every transaction must complete within that window, including all database operations, network round-trips, and processing.
OLAP: Investigative Response Requirements
Analysts have different expectations. They're exploring data, not running workflows:
The key difference: OLAP users are investing time to get answers. An analyst who waits 30 seconds for a query that answers a $10M business question is satisfied. An e-commerce customer who waits 30 seconds to see their cart is gone.
| Requirement | OLTP Target | OLAP Target | Rationale |
|---|---|---|---|
| P50 (Median) | < 10ms | < 5s | Typical user experience baseline |
| P95 | < 50ms | < 30s | Most queries should be responsive |
| P99 | < 100ms | < 2min | Tail latency shapes perception |
| P99.9 | < 500ms | < 10min | Worst-case acceptable |
| Timeout | 1-5s | Hours | When to give up |
| User Impact | Cart abandonment, lost sales | Analyst waits, retries | Business consequence of failure |
OLTP's strict latency requirements drive every design decision: indexed lookups (not full scans), connection pooling (no connection overhead per query), caching layers (avoid database when possible), synchronous replication trade-offs (latency vs. durability). OLAP's relaxed latency allows full table scans, complex joins, and sequential I/O patterns that would be unacceptable for OLTP.
Throughput requirements—how many operations per unit time—differ fundamentally between OLTP and OLAP:
OLTP: High-Frequency, Small Operations
OLTP systems must handle enormous operation counts because each user action translates to database operations:
OLAP: Low-Frequency, Large Operations
OLAP systems handle fewer operations, but each consumes far more resources:
Throughput Measurement:
| Metric | OLTP | OLAP | Implication |
|---|---|---|---|
| Transactions/sec | 1,000-100,000+ | 0.01-10 | OLTP optimizes for commit overhead |
| Queries/sec | 10,000-1,000,000 | 0.1-100 | OLTP queries are simple |
| Rows/sec (read) | 100,000-10M | 1M-100B | OLAP scans dominate |
| Rows/sec (write) | 10,000-1M | 0-10M (batch) | OLTP has continuous writes |
| Concurrent sessions | 1,000-100,000 | 10-1,000 | OLTP serves more users |
| Bytes/sec throughput | 1-100 MB/s | 100MB-10GB/s | OLAP moves more data |
Concurrency Models:
OLTP concurrency focuses on transaction isolation—ensuring thousands of concurrent transactions don't interfere with each other. This requires sophisticated locking, MVCC, or optimistic concurrency control.
OLAP concurrency focuses on resource sharing—ensuring tens of concurrent queries get fair access to CPU, memory, and I/O. This requires query scheduling, resource governors, and workload isolation.
The Critical Difference:
OLTP transactions contend for the same data (two users updating the same row). OLAP queries contend for system resources (two queries competing for CPU and memory). This changes the entire approach to concurrency control.
Running OLAP queries on OLTP systems creates resource contention: the analytical query consumes all I/O bandwidth and buffer cache, starving OLTP transactions. Modern cloud OLAP systems (Snowflake's virtual warehouses) provide workload isolation—separate compute clusters for different query priorities, ensuring critical workloads aren't impacted by expensive analytics.
Consistency requirements reflect how much data correctness matters to the business:
OLTP: Absolute Consistency Required
OLTP data represents the current state of business reality. Errors have immediate, tangible consequences:
For OLTP, consistency is non-negotiable. The database must:
OLAP: Eventual Consistency Acceptable
OLAP data represents historical trends and patterns. Small inconsistencies are often acceptable:
Strong consistency has performance costs: synchronous replication, distributed locks, and coordination overhead. OLTP pays this cost because correctness is paramount. OLAP avoids this cost by accepting data latency (batch loads) and eventual consistency across distributed nodes, enabling higher throughput and lower latency for analytical queries.
Durability concerns how data survives failures. Requirements differ based on data's role and reproducibility:
OLTP: Zero Data Loss Required
OLTP data often cannot be reconstructed:
OLTP durability requirements:
OLAP: Reconstructible Data
OLAP data is typically derived from source systems:
OLAP durability requirements:
Storage Durability:
Both systems require durable storage, but the guarantees differ:
| Requirement | OLTP | OLAP | Reasoning |
|---|---|---|---|
| Data Loss Tolerance (RPO) | 0 seconds | Hours to 1 day | OLTP = source of truth; OLAP = derived |
| Recovery Time (RTO) | < 5 minutes | < 4 hours | OLTP = operations blocked; OLAP = analytics delayed |
| Replication Type | Synchronous | Asynchronous | OLTP needs confirmation before commit |
| Backup Frequency | Continuous + periodic | Daily/weekly | OLTP captures every change |
| Point-in-time Recovery | Yes, to the second | No, to the backup | Audit and compliance needs |
| Disaster Recovery | Hot standby, failover | Cold/warm standby | OLTP needs immediate recovery |
Synchronous durability (write-ahead log flush, synchronous replication) adds significant latency—often 1-5ms per transaction. OLTP systems accept this for correctness. OLAP systems often batch commits and use asynchronous replication, improving throughput at the cost of potential data loss in a narrow window.
Availability requirements reflect the business impact of system downtime:
OLTP: High Availability Critical
OLTP system unavailability directly impacts revenue and operations:
Availability Targets:
OLAP: Flexible Availability
OLAP unavailability delays decisions but rarely blocks operations:
Availability Targets:
| Aspect | OLTP | OLAP |
|---|---|---|
| Failover Strategy | Automatic, sub-minute | Manual or delayed automation OK |
| Replica Configuration | Synchronous hot standby | Asynchronous read replicas |
| Data Loss on Failover | Zero (synchronous) | Some acceptable (async) |
| Geographic Redundancy | Often required (multi-region) | Nice to have |
| Maintenance Windows | None or rolling upgrades | Scheduled windows acceptable |
| Degraded Mode Operation | Read-only mode possible | Full unavailability acceptable |
Per the CAP theorem, in a partition event, you must choose between availability and consistency. OLTP systems typically choose consistency (refuse operations rather than risk inconsistency). OLAP systems can often choose availability (serve stale data rather than refuse queries). This fundamental choice shapes distributed architecture decisions.
The humans and systems that interact with databases have different needs:
OLTP Users: Operational Staff and Applications
OLTP systems serve:
Their requirements:
OLAP Users: Analysts and Business Intelligence
OLAP systems serve:
Their requirements:
OLTP applications know exactly which tables and columns they need—developers write specific queries. OLAP users explore data with ad-hoc queries—analysts need discoverable schemas, documentation, and data cataloging. This drives differences in tooling: OLTP focuses on APIs and ORM; OLAP focuses on SQL editors, BI tools, and data catalogs.
Regulations impose specific requirements on data handling, often differing between operational and analytical contexts:
OLTP: Transaction Audit and Compliance
OLTP systems often fall under strict regulatory requirements:
Requirements impact:
OLAP: Analytics Compliance
OLAP systems have different compliance concerns:
| Requirement | OLTP Focus | OLAP Focus |
|---|---|---|
| Audit Trail | Every transaction logged | Data lineage and access logs |
| Data Retention | Keep originals, define lifecycle | Keep history, archive old data |
| Encryption | All data at rest and transit | Often same, but may exclude some derived data |
| Access Control | Row-level security common | Column/dataset-level security |
| PII Handling | Must protect but keep accurate | Must anonymize, mask, or aggregate |
| Right to Deletion | Must delete or anonymize originals | Must propagate deletions to derived data |
| Data Quality | Enforce at write time | Monitor and alert on quality issues |
GDPR's 'right to be forgotten' creates challenges for OLAP systems. If a customer requests deletion, their data must be removed from operational systems (OLTP), data warehouses (OLAP), and all derived datasets, backups, and archives. This requires end-to-end data lineage and coordinated deletion across systems—a significant architectural consideration.
We have examined the detailed requirements that differentiate OLTP and OLAP systems across every critical dimension. Let's consolidate the key insights:
The Architecture Implication:
These differing requirements make it impossible to optimize a single system for both workloads. Every architectural choice (storage format, indexing, concurrency control, replication) requires choosing between OLTP and OLAP optimization. This is why enterprises maintain separate systems—not due to tradition, but due to fundamentally incompatible requirements.
What's Next:
The final page addresses the practical challenge of serving both sets of requirements: integration challenges between OLTP and OLAP systems. You'll learn about ETL/ELT pipelines, data synchronization strategies, and modern architectures that bridge operational and analytical worlds.
You now understand the specific requirements that drive OLTP and OLAP system design—not just what differs, but why those differences exist and how they cascade into architectural decisions. This requirements-first thinking enables principled technology selection.