![]() ![]() Let’s use the following source table to see how Window View and Live View work in action. Live View can output the computed result by SELECTing from it, or it can push real-time updates using the WATCH query. Live View query processing can be visualised as follows: Instead, it allows you to provide streaming query results for queries where the result of the query can be computed by applying the same query on the current data and the new data separately, and merging the results of the two to compute the final result. On the other hand, Live View, is not tied to any group by function. To better understand Time Window Functions, we can visualise them as follows:įor processed results, the Window View can either push to a specified table or push real-time updates using the WATCH query. The time reference can either be set to the current time, as provided by the now() function, or as a table column. These time window functions are used to group records together over which an aggregate function can be applied. Window View is designed to work with Time Window Functions, in particular Tumble and Hop. ![]() For more details, check out the Window View documentation. However, we haven’t blogged about Window View before, so a quick introduction is necessary. In order for us to examine both Window View and Live View, we must first understand how they work.Īs we have blogged about Live View before, please see our previous articles for more detailed information Making Data Come to Life with ClickHouse Live View Tables, Taking a Closer Look at ClickHouse Live View Tables, Using Live View Tables with a Real Dataset, and, finally, Real-time Moving Average with ClickHouse Live Views. Some typical applications, as pointed out in the documentation, include providing real-time push notifications for query result changes to avoid polling, caching the results of the most frequently used queries, detecting table changes and triggering follow-up queries, as well as real-time monitoring of system table metrics using periodic refresh. On the other hand, Live View is not specialized for time window processing and is meant to provide real-time results for common queries without the need to necessarily group arriving records by time. Typical applications include keeping track of statistics, such as calculated average values over the last 5 minutes or a 1 hour window, which can be used to detect anomalies in a data stream while grouping data by time into one or more active time windows. Window View allows you to compute aggregates over time windows, as records arrive in real-time into the database, and provides similar functionality that is typically found in other stream processing systems. Let’s get a better view on these two different views! General Applications In this article, we will look at both of them to find the differences and similarities between the two. Window Views follow the addition of experimental Live View tables added in 19.14.3.3. In general, the concepts behind Window View can also be found in other systems, such as Azure Stream Analytics, Kafka Streams, and Apache Spark among others. They add another exciting tool for stream processing and analytics to ClickHouse’s toolbox while expanding its collection of view tables. Window Views aggregate data over a period of time, then automatically post at the end of the period. ClickHouse added experimental Window Views starting from 21.12. ![]()
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