CAP theorem
http://en.wikipedia.org/wiki/CAP_theorem
http://www.slideshare.net/alekbr/cap-theorem
The CAP theorem, also known as Brewer's theorem, states that it is impossible for a distributed computer system to simultaneously provide all three of the following guarantees:
Consistency (all nodes see the same data at the same time)
Availability:
a guarantee that every request receives a response about whether it was successful or failed
Node failures don't prevent survivors from continuing to operate
Partition tolerance (the system continues to operate despite arbitrary message loss or failure of part of the system)
According to the theorem, a distributed system cannot satisfy all three of these guarantees at the same time.
You can't have the three at the same time and get an acceptable latency.
http://wiki.apache.org/cassandra/ArchitectureOverview
Cassandra values Availability and Partitioning tolerance (AP). Tradeoffs between consistency and latency are tunable in Cassandra. You can get strong consistency with Cassandra (with an increased latency). But, you can't get row locking: that is a definite win for HBase.
Hbase values Consistency and Partitioning tolerance (CP)
Zookeeper: AP
Google BigTable: CA
Traditional Database: CA
the kernel of truth is that there are trade offs. A system with high partition tolerance and availability (like Cassandra) will sacrifice some consistency in order do it.
https://foundationdb.com/white-papers/the-cap-theorem
Consistency: A read sees all previously completed writes.
Availability: Reads and writes always succeed.
Partition tolerance: Guaranteed properties are maintained even when network failures prevent some machines from communicating with others.
https://cloudant.com/blog/the-cap-theorem/
from Google Plus RSS Feed for 101157854606139706613 http://en.wikipedia.org/wiki/CAP_theorem
via LifeLong Community
http://en.wikipedia.org/wiki/CAP_theorem
http://www.slideshare.net/alekbr/cap-theorem
The CAP theorem, also known as Brewer's theorem, states that it is impossible for a distributed computer system to simultaneously provide all three of the following guarantees:
Consistency (all nodes see the same data at the same time)
Availability:
a guarantee that every request receives a response about whether it was successful or failed
Node failures don't prevent survivors from continuing to operate
Partition tolerance (the system continues to operate despite arbitrary message loss or failure of part of the system)
According to the theorem, a distributed system cannot satisfy all three of these guarantees at the same time.
You can't have the three at the same time and get an acceptable latency.
http://wiki.apache.org/cassandra/ArchitectureOverview
Cassandra values Availability and Partitioning tolerance (AP). Tradeoffs between consistency and latency are tunable in Cassandra. You can get strong consistency with Cassandra (with an increased latency). But, you can't get row locking: that is a definite win for HBase.
Hbase values Consistency and Partitioning tolerance (CP)
Zookeeper: AP
Google BigTable: CA
Traditional Database: CA
the kernel of truth is that there are trade offs. A system with high partition tolerance and availability (like Cassandra) will sacrifice some consistency in order do it.
https://foundationdb.com/white-papers/the-cap-theorem
Consistency: A read sees all previously completed writes.
Availability: Reads and writes always succeed.
Partition tolerance: Guaranteed properties are maintained even when network failures prevent some machines from communicating with others.
https://cloudant.com/blog/the-cap-theorem/
from Google Plus RSS Feed for 101157854606139706613 http://en.wikipedia.org/wiki/CAP_theorem
via LifeLong Community
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