Care to give the Quickwit pitch? Especially on why it is better than Loki.

From my little knowledge of Loki's internals. I think contrary to Loki, Quickwit uses a fully feature search engine library underneath called Tantivy (https://github.com/quickwit-oss/tantivy). Quickwit offers different services (indexer, searcher, ..) that can be ran and scaled independently. It also supports indexing from various sources including file, Rest API, Kafka, Pulsar, Kinesis and more are planned based on community interest. Last but not least, Elasticsearch query API support is being worked on.

Good point.

Unlike index-free solutions like Loki or Parseable, Quickwit is built on top of a modern full-text search index (tantivy). At query time, Quickwit produces much faster results (all other things being equal: CPU, memory, etc.), especially when the volume of data to analyze is large or queries are complex (high cardinality values, aggregations). Quickwit also stores data in a columnar format, so it's also good at OLAP-style queries (no joins though).

This comes with a cost during ingestion; Quickwit is more resource hungry than Loki but can still ingest at 20MB/s to 40 MB/s on a commodity instance with 4CPU. Similarly, regarding storage footprint, Loki compresses logs better because it does not maintain those extra data structures. Still, a Quickwit index tends to be much smaller than an Elasticsearch index.

The next release of Quickwit (may) will be shortly followed by the publication of a benchmark against Elasticsearch/OpenSearch, and by another one later, against Loki. You'll be able to see for yourself.