3 ms·

It is best called "walletscaling." To run anything at scale you are looking at reserved capacity and provisioned throughput. Add extra fees for DynamoDB Acceler

by dsl 6y ago

It is best called "walletscaling." To run anything at scale you are looking at reserved capacity and provisioned throughput. Add extra fees for DynamoDB Accelerator (query caching), Global Tables (cross region replication), backups charged per GB, etc. Most of the required addons aren't in the "cloud model" where you pay for usage, but instead you pay to have them on regardless of usage.

enitihas 6y ago
> DynamoDB Accelerator (query caching) You don't need to use DynamoDB Accelerator though, and it provides value you won't automatically get for free by using another database. If you don't use it you will be managing your own redis/memcache instance with all cache invalidation logic. > Global Tables (cross region replication), Again, you don't get this for free in any other DB. Setting up your own multi master cross regional database is not free. Dynamo DB works fine without any of the above two features. >It is best called "walletscaling. And which DB out of curiosity scales without any load on your wallet? What mythical DB can one run which needs neither horizontal nor vertical scaling, thus not impacting the wallet.
- PopeDotNinja 6y ago
  > Setting up your own multi master cross regional database is not free. Is multi primary the new term? Has an agreed upon term been decided yet? Django seems to be switching to primary/replica. https://docs.djangoproject.com/en/3.0/topics/db/multi-db/ https://docs.djangoproject.com/en/3.0/topics/db/multi-db/
- dsl 6y ago
  > And which DB out of curiosity scales without any load on your wallet? BigQuery includes all these things "for free" in the base price. It is pretty straightforward, with a price for storage and a price for querying (which you can choose to be usage or fixed). DynamoDB started off the same way, and added nickels and times by the roll.
  - enitihas 6y ago
    You can't be serious when comparing BigQuery to DynamoDB. BigQuery is an OLAP database, you can't use it for OLTP workloads. And BigQuery has no equivalents to DAX(very low read latencies), Global Tables(millisecond access to data globally). If you are using BigQuery as an OLTP database with reads and writes going in simultaneously, more power to you.
garethmcc 6y ago
DynamoDB costs you nothing when its not in use. It only charges based on actual reads and writes performed. As opposed to a traditional Relational database that always has to be on and has to be scaled up vertically to the maximum anticipated load permanently and with a read replica if you really want redundancy. DynamoDB gives you redundancy out of the box (your tables are replicated across the three availability zones in a region), the scale is available to you if you have sudden traffic in on demand mode or you can set a limit if you wish to manage costs; your queries may receive errors about being throttled at some point if you approach those limits. For OLTP workloads, DynamoDB (and a lot of other NoSQL-style, cluster based databases) cannot be beat for performance, capacity, scalability and costs. Which is exactly what you want on the front line of a workload that can receive large amounts of traffic. For OLAP workloads with unknown query patterns across a variable set of data that can change over time and large table scans, a relational database is king because the actual volume of traffic is low but the size of queries are a lot larger usually.

by dsl 6y ago

enitihas 6y ago

> DynamoDB Accelerator (query caching) You don't need to use DynamoDB Accelerator though, and it provides value you won't automatically get for free by using another database. If you don't use it you will be managing your own redis/memcache instance with all cache invalidation logic. > Global Tables (cross region replication), Again, you don't get this for free in any other DB. Setting up your own multi master cross regional database is not free. Dynamo DB works fine without any of the above two features. >It is best called "walletscaling. And which DB out of curiosity scales without any load on your wallet? What mythical DB can one run which needs neither horizontal nor vertical scaling, thus not impacting the wallet.

PopeDotNinja 6y ago
> Setting up your own multi master cross regional database is not free. Is multi primary the new term? Has an agreed upon term been decided yet? Django seems to be switching to primary/replica. https://docs.djangoproject.com/en/3.0/topics/db/multi-db/ https://docs.djangoproject.com/en/3.0/topics/db/multi-db/
dsl 6y ago
> And which DB out of curiosity scales without any load on your wallet? BigQuery includes all these things "for free" in the base price. It is pretty straightforward, with a price for storage and a price for querying (which you can choose to be usage or fixed). DynamoDB started off the same way, and added nickels and times by the roll.
- enitihas 6y ago
  You can't be serious when comparing BigQuery to DynamoDB. BigQuery is an OLAP database, you can't use it for OLTP workloads. And BigQuery has no equivalents to DAX(very low read latencies), Global Tables(millisecond access to data globally). If you are using BigQuery as an OLTP database with reads and writes going in simultaneously, more power to you.

garethmcc 6y ago

DynamoDB costs you nothing when its not in use. It only charges based on actual reads and writes performed. As opposed to a traditional Relational database that always has to be on and has to be scaled up vertically to the maximum anticipated load permanently and with a read replica if you really want redundancy. DynamoDB gives you redundancy out of the box (your tables are replicated across the three availability zones in a region), the scale is available to you if you have sudden traffic in on demand mode or you can set a limit if you wish to manage costs; your queries may receive errors about being throttled at some point if you approach those limits. For OLTP workloads, DynamoDB (and a lot of other NoSQL-style, cluster based databases) cannot be beat for performance, capacity, scalability and costs. Which is exactly what you want on the front line of a workload that can receive large amounts of traffic. For OLAP workloads with unknown query patterns across a variable set of data that can change over time and large table scans, a relational database is king because the actual volume of traffic is low but the size of queries are a lot larger usually.