https://www.datastax.com/wp-content/uploads/2013/11/WP-DataStax-Oracle.pdf
Oracle is a solid RDBMS that performs well for the use cases
for which it was designed (e.g. ERP and accounting applications). It is not
architected to tackle the new wave of big data, online applications developed
today. The scale- up, master-slave, non-distributed architecture of
Oracle falls short of what
modern online applications need.
Scalability and Performance Limitations
Oracle’s scale-up,
master-slave design limits both its scalability and performance for servicing
the online elasticity and performance SLA needs of many online applications.
The failure of Oracle to add capacity online in an elastic, scale-out vs.
scale-up manner to service increasing user workloads, keep performance high,
and easily consume fast incoming data from countless geographical locations is
widely recognized.
Benefits of Cassandra
- Massively scalable architecture – a masterless design where all nodes are the same.
- Linear scale performance – online node additions produce predictable increases in performance.
- Continuous availability – redundancy of both data and function mean no single point of failure.
- Transparent fault detection and recovery – easy failed node recovery.
- Flexible, dynamic schema data modeling – easily supports structured, semi-structured, and unstructured data.
- Guaranteed data safety – commit log design ensures no data loss.
- Active everywhere design – all nodes may be written to and read from.
- Tunable data consistency – support for strong or eventual data consistency.
- Multi-data center replication – cross data center and multi-cloud availability zone support for writes/reads built in.
- Data compression – data compressed up to 80% without performance overhead.
- CQL (Cassandra Query Language) – an SQL – like language that makes moving from an RDBMS very easy.
DataStax Cassandra
- Built-in analytics functionality for Cassandra data via integration with a number of Hadoop components (e.g. MapReduce, Hive, Pig, Mahout, etc.)
- Enterprise search capability on Cassandra data via Solr integration.
- Enterprise security including external/internal authentication and object permission management, transparent data encryption, client-tonode and node-to-node encryption, and data auditing.
- Visual cluster management for all administration tasks including backup/restore operations, performance monitoring, alerting, and more.
Cassandra Use Cases
- Time-series data management (e.g. financial, sensor data, web click stream, etc.)
- Online web retail
- Web buyer behavior and personalization management
- Recommendation engines
- Social media input and analysis
- Online gaming
- Fraud detection and analysis
- Risk analysis and management
- Supply chain analytics
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Data Modeling Differences
In traditional databases such as Oracle, data is modeled in
a standard “third normal form” design without the need to know what questions
will be asked of the data.
By contrast, in NoSQL, the questions asked of the
data are what drive the data model design and the data is highly de-normalized.
Data Processing Concerns of Modern Applicaiton
Legacy Application
|
Modern Application
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Slow/medium velocity data
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High velocity data
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Data coming in from one/few locations
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Data coming in from many locations
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Rigid, static structured data
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Flexible, fluid, multi-type data
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Low/medium data volumes; purge often
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High data volumes; retain forever
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Deploy app central location/one server
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Deploy app everywhere/many servers
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Write data in one location
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Write data everywhere/anywhere
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Primary concern: scale reads
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Scale writes and reads
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Scale up for more users/data
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Scale out for more users/data
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DataStax Cassandra vs. Oracle at Functional Level
Feature/Function
|
DataStax/Cassandra
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Oracle RDBMS
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High Availability
|
Continuous availability with built in redundancy and hardware rack
awareness in both single and multiple data centers
|
General replication; Oracle Dataguard (for failover) and Oracle RAC
(single point of failure with storage) bout of which are expensive add-ons. GoldenGate
also offered for certain use cases.
|
Scalability Model
|
Linear performance gains via node additions
|
Scale up via adding CPU’s RAM or Oracle RAC or Exadata
|
Replication Model
|
Peer-to-peer; number of copies configurable across cluster and each
datacenter.
|
Peer-to-peer; number of copies configurable across cluster and each
datacenter
|
Multi-data center/geography/cloud capabilities
|
Multi-directional, 1-many data center support built in, with true
read/write anywhere capability
|
Nothing specific for multi-data center
|
Data partitioning/sharding model
|
Automatic; done via primary key; random or ordered
|
Table partitioning option to enterprise edition; manual server
sharding
|
Data volume support
|
TB-PB capable
|
TB capable; PB with Exadata
|
Analytic support
|
Analytics on Caddadra data via Hadoop integration( MapReduce, Hive,
Pig, Mahout )
|
Analytic functions in Oracle RDBMS via SQL MapReduce. Haddop support
done in NoSQL appliance
|
Enterprise search support
|
Built into dataStax Enterprise via Solr integration
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Handled via Oracle search (cost add-on)
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Mixed workload support
|
All handled in one cluster with built-in workload isolation; no
workload competes for resources with another
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Handled via Oracle Exadata
|
Data Model
|
Google Bigtable like; a wide column store
|
Relational/tabular
|
Flexibility of data model
|
Flexible. Designed for structured, semi-structured, and unstructured
data
|
Rigid; primarily structured data
|
Data consistency mode
|
Tunable consistency (CAP theorem consistency per operation (e.g. per
insert, delete, etc.) across cluster
|
Traditional ACID
|
Transaction Support
|
Provides full Atomic, Isolated, and Durable (AID) transactions
including batch transactions and “lightweight” transactions with Cassandra
2.0 and higher
|
Traditional ACID
|
Security
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Support for all key security needs: Login ID/passwords, external
security support; object permission management; transparent data encryption;
client to mode, node to node encryption; data auditing
|
Full security support
|
Storage model
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Targeted directories with separation (e.g. put some column families
on SSD’s, some on spinning disk)
|
Tablespaces
|
Data compression
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Built in
|
Various methods
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Memory usage model
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Distributed object/row caches across all nodes in a cluster
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Standard data/metadata caches with query cache
|
Logical database container
|
Keyspace
|
Database
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Primary data object
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Column family/table
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Table
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Data variety support
|
Structured, semi-structured, unstructured
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Primarily structured
|
Indexes
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Primary, secondary. Extensible via Solr indexes
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B-Tree, bitmap, clustered, others
|
Core language
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CQL (Cassadra Query Language; resembles SQL)
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SQL
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Primary query utilities
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CQL shell
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SQL*Plus
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Visual query tools
|
DataStax DevCenter and 3rd party support (Aqua data
Studio)
|
SQL Developer from Oracle etc.
|
Development Language support
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Many (Java, C#, Python)
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Many
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Geospatial support
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Done via Solr ingtegration
|
Oracle Geospatial option (cost add-on)
|
Logging (e.g., web, application) data support
|
Handled via log4j
|
Nothing built in
|
Backup/recovery
|
Online, point-in-time restore
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Online, point-in-time restore
|
Enterprise management/monitoring
|
DataStax OpsCenter
|
Oracle Enterprise Manager
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