New Show Hacker News story: Show HN: Kaskada – modern, open-source event processing
Show HN: Kaskada – modern, open-source event processing
5 by benchambers | 0 comments on Hacker News.
We recently open-sourced Kaskada – an event-processing engine built on Rust and Apache Arrow. It features a high-level, declarative query language designed specifically for reasoning about events in bulk and in real time. Unlike SQL, an aggregation over events produces a timeline indicating the value at each point in time over the events that have occurred up to that point in time. The query language provides composable, expression oriented syntax, including the ability to nest aggregations. Due to the focus on events and temporal queries, it provides capabilities to shift events forward (not backward, to avoid "leaking" information about the future into past values), ticks that create new times (allowing you to observe the current value of an aggregation every day, for instance) and joins that operate "at the current time". Example query: # Purchases up to a point in time let purchases_now = count(purchase) # Shift that *forward* one day (so the value now is from 1 day ago) let purchases_yesterday = purchases_now | shift_by(days(1)) # Compute the difference (purchases since yesterday) in purchases_now - purchases_yesterday More on the history of Kaskada is available in this blog post https://ift.tt/oAQOMvN . More information on the project (and documentation) is available at https://kaskada.io .
5 by benchambers | 0 comments on Hacker News.
We recently open-sourced Kaskada – an event-processing engine built on Rust and Apache Arrow. It features a high-level, declarative query language designed specifically for reasoning about events in bulk and in real time. Unlike SQL, an aggregation over events produces a timeline indicating the value at each point in time over the events that have occurred up to that point in time. The query language provides composable, expression oriented syntax, including the ability to nest aggregations. Due to the focus on events and temporal queries, it provides capabilities to shift events forward (not backward, to avoid "leaking" information about the future into past values), ticks that create new times (allowing you to observe the current value of an aggregation every day, for instance) and joins that operate "at the current time". Example query: # Purchases up to a point in time let purchases_now = count(purchase) # Shift that *forward* one day (so the value now is from 1 day ago) let purchases_yesterday = purchases_now | shift_by(days(1)) # Compute the difference (purchases since yesterday) in purchases_now - purchases_yesterday More on the history of Kaskada is available in this blog post https://ift.tt/oAQOMvN . More information on the project (and documentation) is available at https://kaskada.io .
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