Towards a Web-scale Data Management Ecosystem
Demonstrated by SAP HANA
Stefan Bäuerle, Jonathan Dees, Franz Faerber, Wolfgang Lehner
Agenda
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Motivation & Requirements
•
Different Processing Engines and Integration
•
Scale out edition engine
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Application requirements for a modern DBMS
Different:
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data types

consumption models

data models

notions of consistency

application and query language

levels of scaling

hardware capabilities
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HANA Platform
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HANA System
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Beyond relational data processing (1/3)
• Integrate as deep as possible into the engine
Bringing OLAP and OLTP together
Data mining and prediction
Unstructured data
Planning extensions
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Proven: works in thousands of customer systems
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Simplicity: get rid of extracts, loads and redundancy, one system
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OLAP dominates OLTP in real world systems: optimize accordingly
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Examples: Basked analysis, different forecasting algorithms…
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Easy interaction with R and SAS
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Support text search > 30 languages including:
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Stemming, speech tagging, noun extractions, …
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Classification, clustering, named entity recognition, sentinel analysis
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Planning: Define and align business figures for foreseeable future
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Data heavy operators like disaggregation or logical snapshots
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Beyond relational data processing (2/3)
Graph processing
Hierarchy processing
Geospatial processing &
Time series
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Real world business data often resembles graphs
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Model as graph: More explicit and more efficient operators
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Distance, siblings, shortest path, reachability, transitive closure, …
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Special type of general graphs
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Used by almost every business application
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Support for time dependent and versioned hierarchies
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Extended graph operators: level, neighbor, is_ancestor, …
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Native relational data types
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Existing compression techniques + powerful specializations for sensor data
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Spatial: WithinDistance, Contains, Area, …
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Time series: Group by time interval, Interpolate Missing Values, …
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Beyond relational data processing (3/3)
Scientific processing
No SQL processing
Massive scale out
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Bring prominent operators into the engine
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Simplifies and speeds up operations in scientific and financial area
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Matrix operators: Eigenvalue, Multiply, …
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Financial operators: Interest Rates, GarmanKohlagenProcess, …
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Document based models, XML, JSON, …
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Key value stores
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Flexible Schema, in HANA via specific flexible table type
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Conventional business applications fit on single box, but:
there is a new kind of applications requiring massive scale out
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Deep and seamless integration with the Hadoop system
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Scale out and single box application act as one system
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Application integration ( examples )
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Currency conversion

Hierarchy handling
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Aging / dynamic tiering
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Dictionary maintenance

Graph optimizations
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HANA Data Platform
Dynamic Tiering
HANA Dynamic Tiering
 Declare table to use disk storage
 Cost efficient for big data
 Optimized disk based processing powered by IQ
New warm option beside
 Hot (in-memory)
 Cold (Near Linear Storage)
CREATE TABLE „demo“.“SalesOrders_WARM“ (
ID
Integer NOT NULL,
CustomerID
Integer NOT NULL,
OrderDate
date
NOT NULL,
…,
PRIMARY KEY (id)
) USING EXTENDED STORAGE;
INSERT INTO „demo“.“SalesOrders_WARM“ VALUES ( … );
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HANA Data Platform
BigData | Vision
HANA Data Management Platform
Information Management | Text | Search | Graph | Geospatial | Predictive
SAP HANA
In-Memory
HANA
Dynamic Tiering
0.1sec
Instant Results
Warm Data
∞
 Dynamic Tiering
 Smart Data Streaming
 NoSQL | Graph | Geo |
TimeSeries
HADOOP
HANA Scale Out
Infinite Storage
Raw Data
Smart Data Streaming
Administration | Monitoring | Operations | User Management | Security
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HANA native BigData
HANA & Hadoop




SDA  Hive | Spark
MapReduce | HDFS
Admin & Monitoring
User Mgmt / Security
Hadoop Extension
 Velocity Engine
 Integrated with HANA and
Hadoop
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SAP HANA Massive Scale Out Edition (Velocity)
Motivation:
•
Engine for massive scale out and big data
Key Features:
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Scale to thousands of nodes
• Different data freshness and consistency levels
• Efficient fail safety design
•
First class citizen within Hadoop (Spark)
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Support variety of hardware and operating systems
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Extreme query performance by compiling SQL to native code
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SAP HANA SOE (Velocity) and Hadoop (1/2)
Hadoop Ecosystem
Ambari Cluster Management
MLib
Machine
Learning
Hive
SQL
SparkSQL
SQL
Yarn Processing
Spark Processing
HDFS Distributed File System
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HBase
Database
Zookeeper
Coordination
Pig
Scripting
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SAP HANA SOE (Velocity) and Hadoop (2/2)
Steps
 Stage 1: Integration
with Spark (2015)
 Stage 2: Independent
execution cluster
Benefits
 Integration of SAP data
with data lakes
 HANA features add Value
into Hadoop
(e.g. SQL extensions like
time series, hierarchies, …)
 Performance
 Holistic data platform
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Architecture to Support Different Data Freshness Levels
•
Separate component for
Transactions
•
Options
• read your own writes
• up-to-date data vs. certain age
DQP
R
R
Connection 1
(Session data)
R
Transaction Broker
Version Table
Query Engine 2
A, D
R
DTX
…
Connection n
Storage
1
Query Engine 1
A, B, C
Storage … Storage
2
n
Distributed Log
Query Engine 3
A, C, D
…
R
Storage (checkpoints)
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SAP HANA scale out integration
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Conclusion
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Today’s applications have multidimensional set of specialized requirements
•
Gains from moving these requirements into a (single) DBMS:
• Simplified and more explicit data modeling and processing for applications
• Increased performance
• No complicated data transfer between specialized engines
•
Powerful orchestration required
•
Web-scale processing is key to support new applications
SAP HANA strives to answer all these requirements in a single data management platform.
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Thank you
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