Learning Apache Cassandra Quotes

“Using the ALL consistency for both reading and writing, however, is an overkill in virtually all cases. If we write the data at the ALL consistency, then we can subsequently read it with ONE consistency because we know all of the replicas have the most recent copy of the data. Conversely, if we are reading with the ALL consistency, we will have immediate consistency even if the profile was updated with ONE consistency. At least one of the replicas has the most recent version of the data, and since we're reading all the copies, we're guaranteed to get the latest version back in one of them.”
― Mat Brown, Learning Apache Cassandra: Manage Fault Tolerant and Scalable Real-Time Data

“If the data is different on different replicas, Cassandra will return the copy with the most recent timestamp, ensuring that we are getting the most recently written copy of the row.”
― Mat Brown, Learning Apache Cassandra: Manage Fault Tolerant and Scalable Real-Time Data

“Cassandra doesn't only send the request to one replica; instead, it sends the request to all the interested replicas, and then fulfills the request as soon as the first one responds successfully.”
― Mat Brown, Learning Apache Cassandra: Manage Fault Tolerant and Scalable Real-Time Data

“Cassandra's tunable consistency allows us to specify, for each query, how many replicas must be involved for the request to be considered a success.”
― Mat Brown, Learning Apache Cassandra: Manage Fault Tolerant and Scalable Real-Time Data

“In fact, Cassandra is even more robust when a node is unavailable to receive a write. Through a process called hinted handoff, other nodes in the cluster will store information about the write request, and then replay that request to the missing node when it becomes available again.”
― Mat Brown, Learning Apache Cassandra: Manage Fault Tolerant and Scalable Real-Time Data

“Note that master-follower databases are not distributed: every machine has a full copy of the dataset. Master-follower replication is great for scaling up the processing power available for handling read requests, but does nothing to accommodate arbitrarily large datasets. Master-follower replication also provides some resilience against machine failure: in particular, failure of a machine will not result in data loss, since other machines have a full copy of the same dataset.”
― Mat Brown, Learning Apache Cassandra: Manage Fault Tolerant and Scalable Real-Time Data

“process of reading from multiple nodes and collating the results is performed by a coordinator node and is entirely transparent to the application.”
― Mat Brown, Learning Apache Cassandra: Manage Fault Tolerant and Scalable Real-Time Data

“Cassandra tokens are signed 64-bit integers, so the minimum possible hash is -263 or -9223372036854775808 and the maximum possible hash is 263-1 or 9223372036854775807.”
― Mat Brown, Learning Apache Cassandra: Manage Fault Tolerant and Scalable Real-Time Data

“We saw that counter columns can coexist in a table only with other counter columns; they can't be in the same table as other data columns or collection columns.”
― Mat Brown, Learning Apache Cassandra: Manage Fault Tolerant and Scalable Real-Time Data