BigTable
Last updated
Was this helpful?
Last updated
Was this helpful?
Heatmap
Key patterns
Row key design
Choosing a row key that facilitates common queries is of paramount importance to the overall performance of the system. Enumerate your queries, put them in order of importance, and then design row keys that work for those queries.
Granular access control
Some applications or workloads generate significant amount of data. An opportunity raises by storing less data in Cloud Bigtable. As there is a minimum node count applicable for the stored data, reduction in node count is also possible.
HDD can manage more data than SSD. Also, the cost of HDD is lower.
However, the performance characteristics are different for HDD:
read, write latencies are higher
supported reads per second are lower
throughput is lower
In case the topology encompasses more than one cluster, the following are some opportunities:
Each additional cluster results in additional node, data, and a network cost implication. The data will be replicated between all of the clusters in the instance topology.
If instance clusters are located in different regions, the instance will accrue network egress cost inter-region data replication. If an application issues workloads to a cluster in a different region egress cost for both calls from application and response from Bigtable.
One can choose additional clusters in geo-disparate closer to the distributed application endpoints.
Clusters are not required to have a symmetric node count.
vs BigQuery
BigQuery
Bigtable
Cloud Spanner
Optimized for timeseries data, highly available, low-latency
Globally scalable strongly consistent database
Fast wide-range queries
narrow-range queries and have low-latency for this case
analytics database
For IoT data, low-latency writes and scalable to petabytes of data
-
-
Data in a Relational database with worldwide high-performance accessibility. and
Multiple data stores
.
.
.
