3 ms·

Hmm, that's "interesting". Can you elaborate a bit on how yours and theirs differ, if any?

stefansb 9y ago
There is a difference of course. While we are still learning about their implementation I think the statements below are true 1. We don't support just stream. You can throw a SQL at a kafka topic as easy as SELECT * FROM `topic` [WHERE ] 2. We support selecting or filter on the record metadata: offset/timestamp/partition (Haven't seen something similar in Confluent KSQL) 3. We integrate with Schema Registry for Avro. We hope to support Hortonworks schema registry soon as well as protobuf. 4. We allow for injecting fields in the Kafka Key part. For example: SELECT _offset as `_key.offset`, field2 * field3 - abs(field4.field5) as total FROM `magic-topic` 5. Just quickly looking at the Confluent KSQL "abs" function i see it accepts Double only. It doublt that everything is converted to Double before it hits the method and then converted back. (too short of a time to understand the whole implementation). 6. Filters: is related to point 2. We allow filter on message metadata. For example: SELECT * FROM topicA WHERE (a.d.e + b) /c = 100 and _offset > 2000 and partition in (2,6,9) Also not sure if they have customers yet using it. We do.
nehanarkhede 9y ago
There's a pretty big difference to a point that Landoop's KCQL (Kafka Connect Query Language) and Confluent's KSQL (Streaming SQL for Apache Kafka) are two different products. - KSQL is a full-fledged Streaming SQL engine for all kinds of stream processing operations from windowed aggregations, stream-table joins, sessionization and much more. So it does more powerful stream processing on Kafka than what Landoop's product supports which is simple projections and filters. - KSQL can do that because it supports streams and tables as first-class constructs and tightly integrates with Kafka's Streams API and the Kafka log itself. We are not aware of any other products that do that today, including Landoop's tool. - We will add support for Kafka connectors so you can stream data from different systems into Kafka through KSQL. This will cover what Landoop intended with KCQL (Kafka Connect Query Language. - Confluent works with several very large enterprises and many of the companies that have adopted Kafka. We worked with those customers to learn what would solve real business problems and used that feedback to build KSQL. So it . models on real-world customer feedback. - We'd love to hear feedback. Here's the repository https://github.com/confluentinc/ksql https://github.com/confluentinc/ksql and here's the Slack Channel slackpass.io/confluentcommunity - #ksql Hope that helps!
- edan0 9y ago
  KSQL is not a full-fledged streaming SQL engine. If you look at its syntax reference page (https://github.com/confluentinc/ksql/blob/0.1.x/docs/syntax-reference.md#syntax-reference https://github.com/confluentinc/ksql/blob/0.1.x/docs/syntax-...), you can see that it only has a small number of scalar functions, a handful of aggregate functions, and only four major SELECT operators including an invented and nonstandard WINDOW clause (even though ANSI standard SQL has had a WINDOW clause for 18 years, since SQL:99). Contrast that with SQLstream Blaze, which is a full-fledged streaming SQL engine with over a hundred scalar functions, operators, CEP operators/temporal predicates, aggregate functions, analytic functions, UDXes/UDFs, and major statement operators. (See http://sqlstream.com/docs/index.html?conc_transforming_and_analyzing_dat.html http://sqlstream.com/docs/index.html?conc_transforming_and_a... for details.) This all comes into play when an experienced SQL developer wants to write a real-world query or port an existing business application from an RBDMS to a stream processor. Outside of toy applications, this just can't be done with without a full-fledged streaming SQL engine.

There is a difference of course. While we are still learning about their implementation I think the statements below are true 1. We don't support just stream. You can throw a SQL at a kafka topic as easy as SELECT * FROM `topic` [WHERE ] 2. We support selecting or filter on the record metadata: offset/timestamp/partition (Haven't seen something similar in Confluent KSQL) 3. We integrate with Schema Registry for Avro. We hope to support Hortonworks schema registry soon as well as protobuf. 4. We allow for injecting fields in the Kafka Key part. For example: SELECT _offset as `_key.offset`, field2 * field3 - abs(field4.field5) as total FROM `magic-topic` 5. Just quickly looking at the Confluent KSQL "abs" function i see it accepts Double only. It doublt that everything is converted to Double before it hits the method and then converted back. (too short of a time to understand the whole implementation). 6. Filters: is related to point 2. We allow filter on message metadata. For example: SELECT * FROM topicA WHERE (a.d.e + b) /c = 100 and _offset > 2000 and partition in (2,6,9) Also not sure if they have customers yet using it. We do.

nehanarkhede 9y ago

There's a pretty big difference to a point that Landoop's KCQL (Kafka Connect Query Language) and Confluent's KSQL (Streaming SQL for Apache Kafka) are two different products. - KSQL is a full-fledged Streaming SQL engine for all kinds of stream processing operations from windowed aggregations, stream-table joins, sessionization and much more. So it does more powerful stream processing on Kafka than what Landoop's product supports which is simple projections and filters. - KSQL can do that because it supports streams and tables as first-class constructs and tightly integrates with Kafka's Streams API and the Kafka log itself. We are not aware of any other products that do that today, including Landoop's tool. - We will add support for Kafka connectors so you can stream data from different systems into Kafka through KSQL. This will cover what Landoop intended with KCQL (Kafka Connect Query Language. - Confluent works with several very large enterprises and many of the companies that have adopted Kafka. We worked with those customers to learn what would solve real business problems and used that feedback to build KSQL. So it . models on real-world customer feedback. - We'd love to hear feedback. Here's the repository https://github.com/confluentinc/ksql https://github.com/confluentinc/ksql and here's the Slack Channel slackpass.io/confluentcommunity - #ksql Hope that helps!

edan0 9y ago
KSQL is not a full-fledged streaming SQL engine. If you look at its syntax reference page (https://github.com/confluentinc/ksql/blob/0.1.x/docs/syntax-reference.md#syntax-reference https://github.com/confluentinc/ksql/blob/0.1.x/docs/syntax-...), you can see that it only has a small number of scalar functions, a handful of aggregate functions, and only four major SELECT operators including an invented and nonstandard WINDOW clause (even though ANSI standard SQL has had a WINDOW clause for 18 years, since SQL:99). Contrast that with SQLstream Blaze, which is a full-fledged streaming SQL engine with over a hundred scalar functions, operators, CEP operators/temporal predicates, aggregate functions, analytic functions, UDXes/UDFs, and major statement operators. (See http://sqlstream.com/docs/index.html?conc_transforming_and_analyzing_dat.html http://sqlstream.com/docs/index.html?conc_transforming_and_a... for details.) This all comes into play when an experienced SQL developer wants to write a real-world query or port an existing business application from an RBDMS to a stream processor. Outside of toy applications, this just can't be done with without a full-fledged streaming SQL engine.