11/8/2022 0 Comments Snowflake tasks![]() ![]() Now that the Pinot table config contains the Snowflake ingestion task config, the Pinot controller will take care of scheduling Minion tasks. "sql.className": "ai.SnowflakeConnector", \įigure 2: Example StarTree Snowflake connector task configuration within the Pinot table configuration Task Generation "sql.queryTemplate": "SELECT * FROM myTable WHERE timeColumn BETWEEN $START AND $END", \ ![]() "sql.timeColumnFormat": "yyyy-MM-dd'T'HH:mm:ss.SSS'Z'", \ ![]() #Snowflake tasks full#You can view the full list of Snowflake task configs here along with their description. We will introduce these properties as we walk through how the Snowflake Connector works. To use the Snowflake Connector, start by setting the following task configuration into the Pinot table config (Figure 2). In the next section, we will dive deeper into how the connector generates and executes ingestion tasks within Minion.įigure 1: Minion within a Pinot cluster referenced from No-Code Batch Ingestion blog StarTree Snowflake Connector Reference the blog, No-Code Batch Ingestion, which does an excellent job of introducing the Minion framework. By leveraging Minion, the Snowflake connector decouples the Snowflake data ingestion from critical Pinot components which serve queries at low latency. Minion is a native component in Apache Pinot, designed to handle computationally intensive tasks like batch file ingestion, segment creation and deletion, and segment merge and rollup. The Snowflake connector is implemented using the Pinot Minion framework. Universal language: SQL is a familiar language that allows users to express what data they want to be extracted from their Snowflake table at the row and column level.Įxtensibility: a JDBC-based ingestion framework is extensible to other JDBC compliant databases. This allows fine-tuning the ingestion rate to a user’s preference, providing a tradeoff between query cost and rate of ingestion. This approach is beneficial in a few ways:Įxtraction flexibility: Splitting data extraction into batches provides control over how much data to fetch and can allow parallel ingestion when executing queries across multiple connections. ![]() It fetches data in batches from Snowflake by executing SQL queries through the Snowflake JDBC driver. The Snowflake Pinot connector ingests data from Snowflake into Pinot through a pull model. In this blog post, we will go over our new StarTree Snowflake connector which makes it easy to ingest data from Snowflake to Pinot in a self-serve manner. Pinot was purpose-built for serving tens of thousands of SQL queries per second on petabytes of data within milliseconds ( read more here). In addition, data needs to be queried with millisecond latency as it is being generated, which is not currently supported in Snowflake.Īt StarTree, we believe Apache Pinot® is a great solution for real-time analytics which can be used in conjunction with Snowflake (or any other warehouse) for bridging this gap. A query volume like this is prohibitively expensive when using Snowflake. #Snowflake tasks how to#As mentioned in Chinmay Soman’s How To Pick A Real-Time OLAP Platform, user-facing analytics, personalization, and other real-time use cases need the ability to execute tens of thousands of queries per second. #Snowflake tasks Offline#Although it works well for offline analytics use cases such as data exploration and dashboarding, there are gaps when it comes to real-time analytics. It is highly scalable and can run SQL queries on massive amounts of data in an efficient manner. Snowflake is an extremely popular data warehousing technology used primarily for batch analytics. ![]()
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