Posts

With Elasticsearch, indexing data in bulk is a more reliable and scalable approach than indexing a high volume of documents one at time. Here is how to achieve this with AWS Kinesis and a Lambda.

Part one of a four-parter where I’m going to show how to index the complete works of Shakespeare in Elasticsearch. This first part will set up the infrastructure on AWS. The second will go through the lambda that bulk loads data into Elasticsearch. The third will show how to, in Node.js, create the index on the Elasticsearch domain, read the works of Shakespeare from CSV and send to Kinesis, and the fourth will put a .NET 5 Web Api in front of the Elasticsearch cluster.

There is a bug in VS Code that occurs when you use string with @$ - the verbatim character and the interpolation characters, but there is a workaround.

Full source code available here. As I am learning Node.Js I am constantly surprised by how easy it is to do some things, and how difficult to do others, and how poor the examples out there are.

Full source code available here. This is a follow up to my previous post where I used dynamic and JSON files to make querying ElasticSearch with a HttpClient much easier.

Full source code available here. Two recent experiences with C# and JSON frustrated me with how difficult it is to work JSON inside an application. I have also been learning Node.

Full source code available here. This is part 3 of my short introduction to Elasticsearch. In the first part I showed how to create an Elasticsearch index, mapping, and seeded it with data.

Full source code available here. I am learning Node.js and have found it a bit of a struggle to locate good, simple documentation. It feels like most people writing in the space assume a lot of existing knowledge, like you know how plenty of JavaScript, or how to effectively use web browser debug tools, have a good understanding of HTML and CSS.