Table of Contents
Dear reader, in this comprehensive guide, we will fully explore SAP HANA‘s high-performance in-memory architecture. We take a deeper look under the hood to understand the technical innovations that provide extreme speeds for processing transactions and analytics.
Columnar Storage and Compression
As we discussed earlier, columnar storage is one of the major advancements powering SAP HANA‘s performance. But how exactly does organizing data by columns instead of rows help so much?
By storing data column-wise on disk, similar data is kept physically together. This allows very efficient compression schemes to be applied. Some examples include:
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Dictionary Encoding: Unique column values are mapped to integers. Repeated values are replaced with a small code.
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Run Length Encoding: Detects sequences of repeated values and stores them as a single value and count.
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Cluster Encoding: Finds commonly repeating sets of values across rows and stores them once.
These encoding methods can provide compression ratios of 10x or higher, significantly reducing the overall memory and storage footprint. For example, a 250 billion row table requires:
| Storage Type | Space Required |
|---|---|
| Row store | 9.5 TB |
| SAP HANA Column Store | 860 GB |
That‘s over 10x compression just by using columnar storage and encoding! This compact memory footprint is what enables all of the table data to be kept in-memory for faster analysis.
Machine Learning Model Serving
In addition to compression, the columnar stores allow highly efficient analytic processing. Modern applications increasingly use machine learning (ML) to power predictive capabilities. The ML models also need very high throughput and low latency access to the data.
In traditional databases, scoring large volumes of data with complex models could take hours. SAP HANA provides a high-performance serving layer to deploy the ML models directly inside the database. This eliminates data movement and allows blazing fast model scoring on billions of records in minutes rather than hours.
SAP HANA‘s purpose-built architecture reduces model scoring times by over 90% compared to traditional databases.
Benchmarking Row vs Column Stores
To demonstrate the real-world difference columnar architecture makes, let us compare some standard OLTP & OLAP benchmark results on SAP HANA vs a traditional row-store database.
Note: Names masked for simplicity
| Benchmark | Row-Store DB | SAP HANA | Improvement |
|---|---|---|---|
| OLTP Transactions/sec | 170 TPS | 2100 TPS | 11x faster |
| OLAP Queries/hour | 12,200 | 480,000 | 40x faster |
The revolutionary columnar architecture delivers order-of-magnitude improvements in both transactional and analytical performance compared to legacy systems.
Let‘s talk about some more advanced SAP HANA features…
GPU Acceleration
SAP HANA can optionally leverage GPUs (graphical processing units) to massively speed up computation of mathematical and ML algorithms. Certain operations which took hours on CPUs can run in minutes on GPUs.
Common examples include:
- Processing geospatial data
- Financial risk simulations
- Fraud detection models
- Image recognition workloads
Dedicated GPU servers connected to SAP HANA database servers provide an additional level of acceleration for compute-intensive tasks.
Real-Time Data Ingestion
In addition to fast analytical processing, SAP HANA also rapidly ingests and processes huge volumes of real-time data with minimal latency.
The streaming architecture and pushdown optimization allows running event-driven computations on millions of events per second from sources like IoT sensors, web logs etc. This enables real-time analytics on continuously changing data.
Certain types of streaming aggregation queries run over 50 times faster compared to traditional databases. This powers complex event progessing across thousands of live data streams.
Graph Data Processing
Relational databases are only optimized for structured tabular data. But connected datasets like transportation networks, social graphs, parcel deliveries etc. are better represented as mathematical graphs.
SAP HANA has a specialized graph engine that stores nodes and edges natively and processes graph queries in milliseconds. It uses parallel processing and intelligent partitioning to rapidly traverse and analyze dense graph networks with billions of connections.
Key Advantages Over Legacy Analytics Architectures
To summarize, the innovations like columnar storage, parallelism and streaming analytics provide several advantages over traditional EDW and analytical architectures:
- Single Source of Truth: Eliminates need for separate reporting databases through instant analytics directly on source transactions
- No Data Movement: Analysis directly on compressed columns avoids expensive ETL and replication
- Minimal Latency: Enables right-time analytics on real-time data vs batch processing delays
- Unified Analytics: Supports and combines SQL, graph, text, spatial, streaming and ML analytics in one product.
I hope this guide gave you a deeper look at how SAP HANA‘s unique architecture delivers unmatched performance for a wide range of analytical use cases. Please feel free to reach out if you have any other questions!