This blog post will cover important considerations and strategies for choosing the right partition key while migrating from a relational database to DynamoDB. This is an important step in the design and building of scalable and reliable applications on top of DynamoDB.
What is a partition key?
DynamoDB supports two types of primary keys:
- Partition key: Also known as a hash key, the partition key is composed of a single attribute. Attributes in DynamoDB are similar in many ways to fields or columns in other database systems.
- Partition key and sort key: Referred to as a composite primary key or hash-range key, this type of key is composed of two attributes. The first attribute is the partition key, and the second attribute is the sort key
This repository provides the
dqlite Go package, which can be used to replicate a SQLite database across a cluster, using the Raft algorithm.
- No external processes needed: dqlite is just a Go library, you link it it to your application exactly like you would with SQLite.
- Replication needs a SQLite patch which is not yet included upstream.
- The Go Raft package from Hashicorp is used internally for replicating the write-ahead log frames of SQLite across all nodes.
How does it compare to rqlite?
The main differences from rqlite are:
- Full support for transactions
- No need for statements to be deterministic (e.g. you can use
- Frame-based replication instead of statement-based replication, this means in dqlite there’s more data flowing between nodes, so expect lower performance. Should not really matter for most use cases.
The Amazon DynamoDB team is back with another useful feature hot on the heels of encryption at rest. At AWS re:Invent 2017 we launched global tables and on-demand backup and restore of your DynamoDB tables and today we’re launching continuous backups with point-in-time recovery (PITR).
You can enable continuous backups with a single click in the AWS Management Console, a simple API call, or with the AWS Command Line Interface (CLI). DynamoDB can back up your data with per-second granularity and restore to any single second from the time PITR was enabled up to the prior 35 days. We built this feature to protect against accidental writes or deletes. If a developer runs a script against production instead of staging or if someone fat-fingers a DeleteItem call, PITR has you covered. We also built it for the scenarios you can’t normally predict. You can still keep your on-demand backups for as long as needed for archival purposes but PITR works as additional insurance against accidental loss of data. Let’s see how this works.
Today marks the 10 year anniversary of Amazon’s Dynamo whitepaper, a milestone that made me reflect on how much innovation has occurred in the area of databases over the last decade and a good reminder on why taking a customer obsessed approach to solving hard problems can have lasting impact beyond your original expectations.
“Dynamo: Amazon’s Highly Available Key-value Store” received the ACM SIGOPS 2017 Hall of Fame Award
With high throughput, sub-second latency and powerful functionality to automate business processes, BigchainDB looks, acts and feels like a database with added blockchain characteristics.
BigchainDB is a scalable blockchain database. The whitepaper explains what that means.
Today we’re excited to launch Cloud Firestore, a fully-managed NoSQL document database for mobile and web app development. It’s designed to easily store and sync app data at global scale, and it’s now available in beta.
Key features of Cloud Firestore include:
- Documents and collections with powerful querying
- iOS, Android, and Web SDKs with offline data access
- Real-time data synchronization
- Automatic, multi-region data replication with strong consistency
- Node, Python, Go, and Java server SDKs
And of course, we’ve aimed for the simplicity and ease-of-use that is always top priority for Firebase, while still making sure that Cloud Firestore can scale to power even the largest apps.
“To celebrate my sub-30 minute finish on the manitou springs incline, I figured I’d drop some python and pandas knowledge while analyzing the data using beautifulsoup, pandas, python dateutil, python googlemaps, python geopy and the standard library.
I try to look at things in a non-trivial way and reflective of actual problems you encounter analyzing real world data. The data file is here https://gist.github.com/jrjames83/4de9d124e5f43a61be9cb2a… come code along!”