The commercial sector has become a dynamic industry that is reliant on several interwoven metrics, among them big data. Data collection is a billion-dollar industry that influences practically everything in modern society. Accordingly, revenue from Big data alone is expected to hit 68.05 billion dollars by 2025. Why? Most sectors have shifted their focus online, which has led to massive data generated from sensors to customer behavior metrics. The outcome? There is an overwhelming need for Data Analytics and Warehousing Tools capable of storing and analyzing this data. This is where Google Cloud and, more specifically, Google BigQuery, come in handy.
To get the best performance, you can utilize BigQuery Cached Results. It allows you to quickly fetch results for queries that are frequently executed. The BigQuery Cache Results are stored for 24 hours in the cache memory.
In this article, you will learn how to effectively use BigQuery Cache Results.
Table of Contents
- What is Google BigQuery?
- What are BigQuery Cached Results?
- How does the BigQuery Cache work?
What is Google BigQuery?
Google BigQuery is a Data Warehousing solution that stands out from the pack. It utilizes the Google Cloud Platform to offer users a fully managed serverless solution. What’s more, Its cloud-native nature provides a wide array of benefits for users, among them lightning fast capability where you can query on the terabyte-scale in seconds. Another notable benefit is the serverless design which saves users resources. Simply put, you can quickly scale up and down without spending on resources.
Key Features of Google BigQuery
- Integrations: Google BigQuery has many built-in integrations, meaning building a data lake is pretty straightforward.
- Built-in Machine Learning Integration: Not so long ago, deep machine learning knowledge was mandatory for machine learning models on large datasets. However, machine learning integration on Google BigQuery eliminated this as users could build such a database using the tool.
- Multi-Cloud Functionality: BigQuery allows users to analyze and query data stored in more than one cloud platform.
- Automated Data Transfer: With this feature, you can automate data transfer into Google BigQuery on a periodical basis.
By now, you should have a rough idea of what Google BigQuery is and some of the features it offers users. Now let us look at BigQuery Cache results. Take a read below:
What are BigQuery Cached Results?
You need to understand what cached data is for you to get a general idea of BugQuery cached results. Cache memory is temporary storage that keeps data for easier access. You may have come across the term in computers and phones. For the most part, this data is directed into the CPU for easier access to the processor. Now that you have a rough idea of cached data, how does it relate to BigQuery?
As can be inferred from the name, BigQuery cache data results refer to query output stored in the cache memory. What does this mean? Whenever you execute a BigQuery SQL command in Google BigQuery, the results are stored in the cache memory for 24 hours. Therefore, whenever you run the same command within this period, the tool will fetch the results from the cached data. It is worth noting that the BigQuery caching capability is available at a user level, meaning that the feature is only available to you. Hence, if your colleague runs the same query, they will not utilize the same cache results.
Simplify Google BigQuery ETL & Analysis with Hevo’s No-code Data Pipeline
Hevo Data, a No-code Data Pipeline helps to transfer data from 100+ sources to a Data Warehouse like Google BigQuery or a Destination of your choice and visualize it in your desired BI Tool. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without even having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Hevo also supports Google BigQuery as a source. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on the key business needs and perform insightful analysis by using a BI tool of your choice.Get Started with Hevo for Free
Check out why Hevo is the Best:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Connectors: Hevo supports 100+ Integrations to SaaS platforms such as WordPress, FTP/SFTP, Files, Databases, BI tools, and Native REST API & Webhooks Connectors. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt, Data Warehouses; Amazon S3 Data Lakes; Databricks, MySQL, SQL Server, TokuDB, MongoDB, DynamoDB, PostgreSQL Databases to name a few.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
How does the BigQuery Cache work?
Let’s assume you are a BigQuery user and you are running an SQL query. The tool will run the command and fetch the results. This process will automatically create a temporary table and place the output. Therefore, results will be fetched from this temp table when you execute the same query. This saves time since similar queries do not have to be executed twice. It is worth noting that temp tables have no charge since they are deleted after 24 hours.
There exist several scenarios where query results will not be cached, such as the ones listed below:
- When you query a table protected by column-level security, the results may not be cached.
- Query results will not be cached if the timeline has expired (the lifetime is usually 24 hrs).
- Caching does not apply when the destination table is specified in the job configuration, the API, or the command-line tool.
- The results will not be cached if the reference table is changed from the previous cache.
- A query with date-time functions will not be cached since it is subject to change depending o the execution time.
- When you query a table protected by row-level security, the results may not be cached.
You may find yourself in a situation where you do not want to utilize the BigQuery cache capabilities. So, how do you turn it off? It’s pretty easy. Just follow the guidelines below:
- Step 1: Head on to the BigQuery WebUI.
- Step 2: Click on ‘More’ and select ‘Query Settings.’
- Step 3: There is a check box labeled ‘Use Cached Results,’ uncheck it and select ‘Save.’
That’s it. By following the outline above, you will have disabled BiQuery cache settings. However, it is worth noting that best practice dictates using these settings for you to get the most out of BigQuery’s caching capability. You can ensure that going through the following aspects:
1. Ensuring the use of BigQuery Cache
You can compel all tasks in BigQuery to use cache and fail when this setting is unavailable. Here is how you go about the process:
- Step 1: For API users, all you have to do is head on to query and set the createDisposition property to CREATE_NEVER.
- Step 2: For cloud console users, you can include the require_cache flag in your code to ensure that the query uses cache as shown below:
bq query --require_cache
2. Verifying the use of BigQuery Cache
Depending on your query, there are two distinct ways you can verify the use of cache.
- Step 1: For API users, the cacheHit property should be set to true.
- Step 2: For Cloud Console users, the string should display ‘cached.’
In this post, you learned what BigQuery Cached results are and how they may benefit you. You also learned how the BigQuery Cache works and why you should utilize this feature. What’s more, you might have found yourself in a situation where your results may not have been cached. It significantly improves query performance, especially when you run repetitive queries.
As you collect and manage your data across several applications and databases in your business, it is important to consolidate it for a complete performance analysis of your business. However, it is a time-consuming and resource-intensive task to continuously monitor the Data Connectors. To achieve this efficiently, you need to assign a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse like Google BigQuery, BI Tool, or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-based ETL tool such as Hevo Data.Visit our Website to Explore Hevo
Hevo Data, a No-code Data Pipeline can seamlessly transfer data from a vast sea of 100+ sources to a Data Warehouse like Google BigQuery, BI Tool, or a Destination of your choice. Hevo also supports Google BigQuery as a Source. It is a reliable, completely automated, and secure service that doesn’t require you to write any code!
If you are using BigQuery as your Data Warehousing & Analytics platform and searching for a no-fuss alternative to Manual Data Integration, then Hevo can effortlessly automate this for you. Hevo, with its strong integration with 100+ sources and BI tools(Including 40+ Free Sources), allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.
Tell us about your experience of using the BigQuery Cache Results! Share your thoughts with us in the comments section below.