Snowflake parameterized query


  • Execute queries in parallel against Snowflake using AWS Glue
  • The Four Parts of a Snowflake Query
  • How to Write Parameterized Queries in Snowflake?
  • Dynamic M Parameters, Snowflake Native SQL And Paginated Reports
  • Snowflake Parameters Insight
  • statement_timeout_in_seconds snowflake
  • Execute queries in parallel against Snowflake using AWS Glue

    Mazen Bahsoun Recently, I was working with a customer that wanted to pull large amounts of data from multiple databases hosted in many locations into Amazon S3. Running glue jobs on these data sources in parallel required crawling these databases—something we deemed not feasible. In most instances, we also needed to execute a specific query containing filters and joins.

    In other words, the solution had to be flexible, reliable, and scalable. I had to figure out how to address reading the data in parallel using only AWS serverless services to keep job runs short and cost manageable. AWS Glue 2. Glue excels at loading entire tables and allows the ability to leverage Spark processing, functions, and SQL queries.

    This capability makes it suitable for large scale transformations without needing to manage any infrastructure.

    But in most cases, especially in a change data capture scenario, we only need to return a subset of a table, especially when that table contains billions of records. This would not only ease the load on the source database but would also save on job run time and consequently cost. We will be modularly building the glue job following these steps: Instantiate the Spark environment Configure a connection to Snowflake Execute the query Parallelize reads using a partition column Write to Amazon S3 Instantiate the Spark environment We first start by importing some necessary libraries: from pyspark import SparkConf, SparkContext from pyspark.

    Unlike Glue Databrew and Glue Studio, we do not need to create a connector or connection ahead of time. We will instead instantiate the connection inside the job. To connect to a Snowflake warehouse from Glue 2. I ended up using the snowflake-jdbc After downloading the jar files, we need to upload them to an S3 bucket that the Glue Job can access. After all, the whole appeal of using Glue is scalability without worrying about infrastructure. Parallelize reads using a partition column To utilize all the available executors for our job and read data in parallel, we need to setup a few options for our spark reader API: partitionColumn is the name of a column used for partitioning.

    It must be of integer, date, or timestamp type. The best partition columns are ID, primary key, row number, or any column that is as evenly distributed as possible. This will also be the number of concurrent connections to the database. When setting this up, we ideally want one partition per executor. I use the number of DPUs as a guide with each having two executors. Assuming the partitionColumn is evenly distributed, the written files should be almost identical in size. Read more about spark writing modes here.

    Glue Studio also offers a way to run queries against custom connections but as of the time of writing this blog post, it is not possible to run Glue Studio jobs from AWS Step Functions or pass the query dynamically.

    I will, nonetheless, be covering the Glue Studio approach in a following blog post and compare the two approaches. If you feel the process is too complex and could use extra help, feel free to contact an AWS expert here at 1Strategy. Reach out to us at info 1strategy. Recent Blog Posts.

    The Four Parts of a Snowflake Query

    What is different is how Snowflake goes about the query process to make it more modular and configurable. While this gives you more flexibility to query your data, it can also result in vastly different query times and even return different query results based on the warehouse you choose and the security role you define as part of the query. In this article, you will learn the four parts of a Snowflake query, how it unlocks greater query flexibility, and how this differs from legacy data warehouses.

    To kickstart your journey to the Snowflake Data Cloud, check out this guide for practical tips and guidelines to consider. The Four Parts of a Snowflake Query When logging into the Snowflake Data Cloud and attempting to query data, you will need to select four pieces of context to do so. As a user, you can assume or change to various roles that have been granted to your user account. Learn more about Snowflake Roles and Access Control. Warehouse The next piece of context you will need to select is a warehouse.

    A warehouse is a set of compute resources. Default warehouses have one cluster of resources behind a warehouse, but Enterprise Edition accounts can have multi-cluster warehouses. Multi-cluster warehouses are used to control user and query concurrency. A warehouse is technically known as a virtual warehouse because no data is associated with the warehouse itself, only compute resources.

    The size of the cluster behind the warehouse is an important tuning parameter for both performance and cost. One simple rule is that each warehouse is twice as large as the previous warehouse and query performance roughly follows the same scale.

    Database and Schema A database belongs to exactly one Snowflake account and contains schemas. While a schema belongs to exactly one database and contains database objects such as tables, views, etc. In Snowflake when you want to query, you have to choose both a Database and Schema. Together a database and schema are called a namespace. Unlike Oracle where a schema is confusingly equal to a user , a schema is simply another level of organizing tables.

    This means the full coordinates to a table in Snowflake require the database, schema, and table name. However, I prefer the two-level hierarchy of database and schema because it ends up being simpler to use and easy to understand.

    After all pieces of context are selected, you can run a query from the worksheet. It is worth noting that while role and warehouse must be specified in the context to run a query, database and schema can be specified within the query itself.

    Now you understand the basic structure of Snowflake. Go forth and query! This is a tool for log collecting, monitoring, and alert management that we offer as part of our services. And, as part of our Cloud DataOps service , we also provide Snowflake Monitoring and Dashboard reporting to monitor query, credit, and usage consumption from your Snowflake environment.

    Common Snowflake Query Questions Now that I know the critical parts of a Snowflake query, how can I determine how many credits each query uses? Including auto suspend time, concurrent query usage, and whether or not the query requires a warehouse. But, the general way of finding this out is by taking the query execution time and multiplying it by the size of the warehouse. Now that I can execute a query, how do I see the results? If you are accessing Snowflake via a BI tool or other connection, the results will show up to populate a report, dashboard, or variable for code execution.

    Now that I have executed a query, how do I view the list of queries that I have run in the past? You can further customize history by expanding search parameters to find what you are interested in. How can I compare the query performance when I change warehouses? First off, this requires another warehouse to exist, but in most organizations there are options for almost all warehouse types. To compare the performance between two separate warehouses, simply execute a query starting in one warehouse and then execute in another.

    By analyzing the query history you will be able to see the execution time as well as the explain plan to see how warehouses act on the same data. How can I track the performance of the query over time as the table grows in size?

    This is a bit more of a complicated ask because it requires more than looking through the history table. There are queries that you can run against system tables to estimate how this is tracking over time.

    But the ultimate solution requires metadata and pipeline tracking to ensure you are comparing the correct query and process. Share on linkedin.

    How to Write Parameterized Queries in Snowflake?

    To connect to a Snowflake warehouse from Glue 2. I ended up using the snowflake-jdbc After downloading the jar files, we need to upload them to an S3 bucket that the Glue Job can access. After all, the whole appeal of using Glue is scalability without worrying about infrastructure. Parallelize reads using a partition column To utilize all the available executors for our job and read data in parallel, we need to setup a few options for our spark reader API: partitionColumn is the name of a column used for partitioning.

    It must be of integer, date, or timestamp type. The best partition columns are ID, primary key, row number, or any column that is as evenly distributed as possible. This will also be the number of concurrent connections to the database. When setting this up, we ideally want one partition per executor. I use the number of DPUs as a guide with each having two executors. Common Snowflake Query Questions Now that I know the critical parts of a Snowflake query, how can I determine how many credits each query uses?

    Including auto suspend time, concurrent query usage, and whether or not the query requires a warehouse. But, the general way of finding this out is by taking the query execution time and multiplying it by the size of the warehouse. Now that I can execute a query, how do I see the results? If you are accessing Snowflake via a BI tool or other connection, the results will show up to populate a report, dashboard, or variable for code execution.

    Now that I have executed a query, how do I view the list of queries that I have run in the past? You can further customize history by expanding search parameters to find what you are interested in. How can I compare the query performance when I change warehouses? First off, this requires another warehouse to exist, but in most organizations there are options for almost all warehouse types. To compare the performance between two separate warehouses, simply execute a query starting in one warehouse and then execute in another.

    By analyzing the query history you will be able to see the execution time as well as the explain plan to see how warehouses act on the same data. How can I track the performance of the query over time as the table grows in size? This is a bit more of a complicated ask because it requires more than looking through the history table. There are queries that you can run against system tables to estimate how this is tracking over time.

    Industry Benchmarks and Competing with Integrity. Pruning: The query returned the entire table consisting of over 15, micro-partitions. Thanks for contributing an answer to Stack Overflow!

    Dynamic M Parameters, Snowflake Native SQL And Paginated Reports

    Help us improve this article with your feedback. I will not be updating this blog anymore but will continue with new contents in the Snowflake world! Maximum time to batch transactions before applying seconds : The maximum time to collect transactions in batches before declaring a timeout.

    This is a session type parameter which tells Snowflake how many seconds should it wait for acquiring a resource lock before timing out and aborting the query. Somehow the Task is not running inside the defined time limit of seconds. What does the word labor mean in this context? Snowflake offers various payment plans for their cloud data platform. Are the "bird sitting on a live wire" answers wrong? The default value is The timeout field specifies that the server allows 60 seconds for the statement to be executed.

    What is the difference between a linear regulator and an LDO. Between the reduction in operational complexity, the pay-for-what-you-use pricing model, and the ability to isolate compute workloads there are numerous ways to reduce costs associated with … This engine does not support the REPLACE. Snowflake provides the following object parameters: Also a session parameter i.

    For inheritance and override details, see the parameter description. Also a session parameter i. Snowflake Date and Time Data Types. When a user is killing the Spark jobsthe current Snowflake query keeps on running for many hours Thank you. The date Functions are sub-divided into 7 types of functions. I am using snowflake sdk to to query the snowflake data base, we are also streaming the result we are getting from snowflake.

    A warehouse has a timeout of seconds. This parameter will help to control end users and prevent bad and heavy Straightening out your database isn't a disaster to recover from with Snowflake's Time Travel. The allowed idle time for a connection before it is closed. This section provides a complete list of the options you can configure in the connection string for this provider.

    Query has timed out. Asking for help, clarification, or responding to other answers. Compute usage is billed to users on a per second basis, minimum being 60 seconds. Connect and share knowledge within a single location that is structured and easy to search. Is it rude to say "Speak of the devil- Here is Grandma now! If you are going to implement … Are there countries that ban public sector unions, but allow private sector ones? The above statement uses offset time in seconds.

    Required properties are listed under the Settings tab. Create a database from a share provided by another Snowflake account. The Snowflake Data Cloud was designed with the cloud in mind, and allows its users to interface with the software without having to worry about the infrastructure it runs on or how to install it.

    I'm not sure this really helps me understand why terminating a single session also forces a logout of the user from where the session was generated. As an object type, it can be applied to warehouses. IT determines. Or anyway, solve my problem. It takes still much time but works in the end. Pre-purchased Snowflake capacity plans are also available. Pool Max Size: The maximum connections in the pool. Auto Resume.

    Snowflake Parameters Insight

    Can you see the shadow of a spaceship on the Moon while looking towards the Earth? You might choose different default settings for each, but regardless, it is essential to consider which configuration will work best for your organization. Here are the commands to suspend or resume a virtual warehouse. At the same time, as soon as your Snowflake Account is provisioned, Make use of procedural logic via … Pre-purchased Snowflake capacity plans are also available.

    statement_timeout_in_seconds snowflake

    Omitted current job as forgot to send updated CV and got job offer. Default is 0 no timeout. Finally, set up the Snowflake deployment to work well in your entire data ecosystem.


    thoughts on “Snowflake parameterized query

    • 14.08.2021 at 18:41
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      What entertaining question

      Reply
    • 15.08.2021 at 02:04
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      Have quickly answered :)

      Reply
    • 17.08.2021 at 00:17
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      It does not approach me. There are other variants?

      Reply

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