For instance you can notice when you run command like: There is no virtual warehouse visible in history tab, meaning that this information is retrieved from metadata and as such does not require running any virtual WH! The bar chart above demonstrates around 50% of the time was spent on local or remote disk I/O, and only 2% on actually processing the data. The query result cache is also used for the SHOW command. Snowflake architecture includes caching layer to help speed your queries. The queries you experiment with should be of a size and complexity that you know will Designed by me and hosted on Squarespace. These are:- Result Cache: Which holds the results of every query executed in the past 24 hours. There are 3 type of cache exist in snowflake. Compare Hazelcast Platform and Veritas InfoScale head-to-head across pricing, user satisfaction, and features, using data from actual users. Clearly data caching data makes a massive difference to Snowflake query performance, but what can you do to ensure maximum efficiency when you cannot adjust the cache? Bills 1 credit per full, continuous hour that each cluster runs; each successive size generally doubles the number of compute Styling contours by colour and by line thickness in QGIS. Learn about security for your data and users in Snowflake. Is there a proper earth ground point in this switch box? This is an indication of how well-clustered a table is since as this value decreases, the number of pruned columns can increase. This article provides an overview of the techniques used, and some best practice tips on how to maximize system performance using caching. following: If you are using Snowflake Enterprise Edition (or a higher edition), all your warehouses should be configured as multi-cluster warehouses. Some operations are metadata alone and require no compute resources to complete, like the query below. Metadata cache : Which hold the object info and statistic detail about the object and it always upto date and never dump.this cache is present. Small/simple queries typically do not need an X-Large (or larger) warehouse because they do not necessarily benefit from the 1 or 2 Snowflake then uses columnar scanning of partitions so an entire micro-partition is not scanned if the submitted query filters by a single column. This query plan will include replacing any segment of data which needs to be updated. Imagine executing a query that takes 10 minutes to complete. The first time this query is executed, the results will be stored in memory. To How can I get the range of values, min & max for each of the columns in the micro-partition in Snowflake? This button displays the currently selected search type. The screenshot shows the first eight lines returned. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. In total the SQL queried, summarised and counted over 1.5 Billion rows. Warehouse provisioning is generally very fast (e.g. if result is not present in result cache it will look for other cache like Local-cache andit only go dipper(to remote layer),if none of the cache doesn't hold the required result or when underlying data changed. We will now discuss on different caching techniques present in Snowflake that will help in Efficient Performance Tuning and Maximizing the System Performance. the larger the warehouse and, therefore, more compute resources in the >> In multicluster system if the result is present one cluster , that result can be serve to another user running exact same query in another cluster. This makesuse of the local disk caching, but not the result cache. If you never suspend: Your cache will always bewarm, but you will pay for compute resources, even if nobody is running any queries. The query result cache is the fastest way to retrieve data from Snowflake. You can unsubscribe anytime. Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, When the query is executed again, the cached results will be used instead of re-executing the query. This tutorial provides an overview of the techniques used, and some best practice tips on how to maximize system performance using caching, Imagine executing a query that takes 10 minutes to complete. available compute resources). All DML operations take advantage of micro-partition metadata for table maintenance. SELECT CURRENT_ROLE(),CURRENT_DATABASE(),CURRENT_SCHEMA(),CURRENT_CLIENT(),CURRENT_SESSION(),CURRENT_ACCOUNT(),CURRENT_DATE(); Select * from EMP_TAB;-->will bring data from remote storage , check the query history profile view you can find remote scan/table scan. The Results cache holds the results of every query executed in the past 24 hours. (Note: Snowflake willtryto restore the same cluster, with the cache intact,but this is not guaranteed). Same query returned results in 33.2 Seconds, and involved re-executing the query, but with this time, the bytes scanned from cache increased to 79.94%. Snowflake Documentation Getting Started with Snowflake Learn Snowflake basics and get up to speed quickly. once fully provisioned, are only used for queued and new queries. Innovative Snowflake Features Part 1: Architecture, Number of Micro-Partitions containing values overlapping with each together, The depth of overlapping Micro-Partitions. This can be especially useful for queries that are run frequently, as the cached results can be used instead of having to re-execute the query. Do new devs get fired if they can't solve a certain bug? What am I doing wrong here in the PlotLegends specification? This can be done up to 31 days. been billed for that period. 1 Per the Snowflake documentation, https://docs.snowflake.com/en/user-guide/querying-persisted-results.html#retrieval-optimization, most queries require that the role accessing result cache must have access to all underlying data that produced the result cache. This data will remain until the virtual warehouse is active. running). Not the answer you're looking for? X-Large multi-cluster warehouse with maximum clusters = 10 will consume 160 credits in an hour if all 10 clusters run Finally, unlike Oracle where additional care and effort must be made to ensure correct partitioning, indexing, stats gathering and data compression, Snowflake caching is entirely automatic, and available by default. Starting a new virtual warehouse (with no local disk caching), and executing the below mentioned query. To understand Caching Flow, please Click here. By all means tune the warehouse size dynamically, but don't keep adjusting it, or you'll lose the benefit. Snowflake will only scan the portion of those micro-partitions that contain the required columns. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Clearly any design changes we can do to reduce the disk I/O will help this query. Reading from SSD is faster. Are you saying that there is no caching at the storage layer (remote disk) ? Set this value as large as possible, while being mindful of the warehouse size and corresponding credit costs. The underlying storage Azure Blob/AWS S3 for certain use some kind of caching but it is not relevant from the 3 caches mentioned here and managed by Snowflake. Snowflake holds both a data cache in SSD in addition to a result cache to maximise SQL query performance. (and consuming credits) when not in use. and simply suspend them when not in use. This is maintained by the query processing layer in locally attached storage (typically SSDs) and contains micro-partitions extracted from the storage layer. Snowflake uses a cloud storage service such as Amazon S3 as permanent storage for data (Remote Disk in terms of Snowflake), but it can also use Local Disk (SSD) to temporarily cache data used by SQL queries. Result Set Query:Returned results in 130 milliseconds from the result cache (intentially disabled on the prior query). When considering factors that impact query processing, consider the following: The overall size of the tables being queried has more impact than the number of rows. When a query is executed, the results are stored in memory, and subsequent queries that use the same query text will use the cached results instead of re-executing the query. Snowflake caches and persists the query results for every executed query. Multi-cluster warehouses are designed specifically for handling queuing and performance issues related to large numbers of concurrent users and/or While you cannot adjust either cache, you can disable the result cache for benchmark testing. For more information on result caching, you can check out the official documentation here. Snowflake. Demo on Snowflake Caching : Hope this blog help you to get insight on Snowflake Caching. Run from warm: Which meant disabling the result caching, and repeating the query. multi-cluster warehouse (if this feature is available for your account). Snowflake utilizes per-second billing, so you can run larger warehouses (Large, X-Large, 2X-Large, etc.) Snowflake also provides two system functions to view and monitor clustering metadata: Micro-partition metadata also allows for the precise pruning of columns in micro-partitions. Metadata cache Snowflake stores a lot of metadata about various objects (tables, views, staged files, micro partitions, etc.) Stay tuned for the final part of this series where we discuss some of Snowflake's data types, data formats, and semi-structured data! Results cache Snowflake uses the query result cache if the following conditions are met. In the following sections, I will talk about each cache. Instead, It is a service offered by Snowflake. Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged In other words, consider the trade-off between saving credits by suspending a warehouse versus maintaining the you may not see any significant improvement after resizing. Keep this in mind when choosing whether to decrease the size of a running warehouse or keep it at the current size. even if I add it to a microsoft.snowflakeodbc.ini file: [Driver] authenticator=username_password_mfa. Before using the database cache, you must create the cache table with this command: python manage.py createcachetable. If a warehouse runs for 61 seconds, shuts down, and then restarts and runs for less than 60 seconds, it is billed for 121 seconds (60 + 1 + 60). Metadata cache Query result cache Index cache Table cache Warehouse cache Solution: 1, 2, 5 A query executed a couple. Using Kolmogorov complexity to measure difficulty of problems? The diagram below illustrates the levels at which data and results are cached for subsequent use. or events (copy command history) which can help you in certain situations. Some operations are metadata alone and require no compute resources to complete, like the query below. Micro-partition metadata also allows for the precise pruning of columns in micro-partitions. There are some rules which needs to be fulfilled to allow usage of query result cache. Starting a new virtual warehouse (with Query Result Caching set to False), and executing the below mentioned query. >>This cache is available to user as long as the warehouse/compute-engin is active/running state.Once warehouse is suspended the warehouse cache is lost. Service Layer:Which accepts SQL requests from users, coordinates queries, managing transactions and results. It's important to check the documentation for the database you're using to make sure you're using the correct syntax. Leave this alone! To put the above results in context, I repeatedly ran the same query on Oracle 11g production database server for a tier one investment bank and it took over 22 minutes to complete. Warehouses can be set to automatically resume when new queries are submitted. These are available across virtual warehouses, so query results returned to one user is available to any other user on the system who executes the same query, provided the underlying data has not changed. Some operations are metadata alone and require no compute resources to complete, like the query below. I have read in a few places that there are 3 levels of caching in Snowflake: Metadata cache. Snowflake supports resizing a warehouse at any time, even while running. Snowflake uses the three caches listed below to improve query performance. Caching in virtual warehouses Snowflake strictly separates the storage layer from computing layer. SELECT BIKEID,MEMBERSHIP_TYPE,START_STATION_ID,BIRTH_YEAR FROM TEST_DEMO_TBL ; Query returned result in around 13.2 Seconds, and demonstrates it scanned around 252.46MB of compressed data, with 0% from the local disk cache. Snowflake will only scan the portion of those micro-partitions that contain the required columns. When there is a subsequent query fired an if it requires the same data files as previous query, the virtual warhouse might choose to reuse the datafile instead of pulling it again from the Remote disk, This is not really a Cache. >>To leverage benefit of warehouse-cache you need to configure auto_suspend feature of warehouse with propper interval of time.so that your query workload will rightly balanced. To disable auto-suspend, you must explicitly select Never in the web interface, or specify 0 or NULL in SQL. I have read in a few places that there are 3 levels of caching in Snowflake: Metadata cache. Caching Techniques in Snowflake. Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is Finally, results are normally retained for 24 hours, although the clock is reset every time the query is re-executed, up to a limit of 30 days, after which results query the remote disk. For more information on result caching, you can check out the official documentation here. This cache is dropped when the warehouse is suspended, which may result in slower initial performance for some queries after the warehouse is resumed. However, the value you set should match the gaps, if any, in your query workload. : "Remote (Disk)" is not the cache but Long term centralized storage. This level is responsible for data resilience, which in the case of Amazon Web Services, means 99.999999999% durability. It contains a combination of Logical and Statistical metadata on micro-partitions and is primarily used for query compilation, as well as SHOW commands and queries against the INFORMATION_SCHEMA table. The sequence of tests was designed purely to illustrate the effect of data caching on Snowflake. Quite impressive. Typically, query results are reused if all of the following conditions are met: The user executing the query has the necessary access privileges for all the tables used in the query. select * from EMP_TAB;-->data will bring back from result cache(as data is already cached in previous query and available for next 24 hour to serve any no of user in your current snowflake account ). Snowflake Architecture includes Caching at various levels to speed the Queries and reduce the machine load. Second Query:Was 16 times faster at 1.2 seconds and used theLocal Disk(SSD) cache. Unlike many other databases, you cannot directly control the virtual warehouse cache. If a user repeats a query that has already been run, and the data hasnt changed, Snowflake will return the result it returned previously. Now if you re-run the same query later in the day while the underlying data hasnt changed, you are essentially doing again the same work and wasting resources. Absolutely no effort was made to tune either the queries or the underlying design, although there are a small number of options available, which I'll discuss in the next article. The interval betweenwarehouse spin on and off shouldn't be too low or high. LinkedIn and 3rd parties use essential and non-essential cookies to provide, secure, analyze and improve our Services, and (except on the iOS app) to show you relevant ads (including professional and job ads) on and off LinkedIn. The tests included:-. may be more cost effective. Did you know that we can now analyze genomic data at scale? This topic provides general guidelines and best practices for using virtual warehouses in Snowflake to process queries. Metadata Caching Query Result Caching Data Caching By default, cache is enabled for all snowflake session. This query returned in around 20 seconds, and demonstrates it scanned around 12Gb of compressed data, with 0% from the local disk cache. SELECT TRIPDURATION,TIMESTAMPDIFF(hour,STOPTIME,STARTTIME),START_STATION_ID,END_STATION_IDFROM TRIPS; This query returned in around 33.7 Seconds, and demonstrates it scanned around 53.81% from cache. (c) Copyright John Ryan 2020. However, provided you set up a script to shut down the server when not being used, then maybe (just maybe), itmay make sense. Thanks for contributing an answer to Stack Overflow! Some of the rules are: All such things would prevent you from using query result cache.