Along with cost and feature set, performance is always a common decision metric among prospective users choosing between log analytics solutions. Those who have used Elasticsearch want to maintain the snappy response times for text search requests. Others using relational databases or data warehouses are concerned with how those relational-type queries perform within a non-relational system. We’ll get into those types of comparisons in later articles. For now, we will focus on something important to everyone: ingestion speed and size on disk. Ingestion speed will impact how long it takes to migrate to a new solution, as well as how much that solution will have to scale for incoming data. Size on disk translates directly to cost and retention period.
Our focus from the start has been building a high performance distributed architecture that can take advantage of modern cloud computing design patterns, most notably scaling in/out with different sized workloads. However, our underlying data fabric and unique data representation, called Data Edge, is not a brute force solution – we don’t just throw horsepower behind it. Data Edge was designed from the ground up to be fast and compact, not just one use case, but several. In order to maintain this performance through product iterations, we routinely benchmark against other technologies.
Our goal is to be:
- As fast as an RDBMS for data ingestion
- On par with an RDBMS relational operations (order, join, group by)
- As fast as Elasticsearch for text search
- As small on disk as a column store
- Lower cost of ownership than everything else
Read the entire article here, Elastic vs. RedShift vs. Chaos Sumo
via the fine folks at Chaos Sumo