Space management and direct path load are important considerations in ClickHouse for optimizing storage efficiency and data loading performance. Here are some tips and tricks for space management and direct path load in ClickHouse: Space Management: Direct Path Load: By employing these tips and tricks for space management and direct path load in ClickHouse, you […]
In ClickHouse, evaluating the accuracy of cardinality estimates in a query plan can be challenging since ClickHouse relies on different heuristics and sampling techniques to estimate cardinalities. However, you can take the following steps to gain insights into the accuracy of cardinality estimates: It’s important to note that ClickHouse’s cardinality estimation is not always exact […]
To drop an existing histogram on a ClickHouse column and prevent the Auto Stats gathering job from creating it in the future, you can follow these steps: For example: DESCRIBE TABLE your_table; Identify the column name that has the histogram. ALTER TABLE your_table MODIFY COLUMN your_column CLEAR HISTOGRAM; Replace your_table with the name of your […]
To display and read the execution plans for a SQL statement in ClickHouse, you can follow these steps using real-life data sets: For this example, we’ll use a simple SELECT statement to retrieve data from the orders table: SELECT customer_id, COUNT(*) AS order_countFROM ordersGROUP BY customer_idORDER BY order_count DESC; This query will calculate the number […]
To identify overlapping date ranges in ClickHouse, you can use SQL queries that compare the start and end dates of each range to determine if there are any overlaps. Here’s an example using a real-life dataset of employee vacation schedules: Assume we have a ClickHouse table called “vacation_schedule” with the following structure: CREATE TABLE vacation_schedule […]
Grouping, rollup, and cube are SQL query operations that allow for grouping and aggregation of data based on multiple dimensions or attributes. In ClickHouse, these operations are implemented using the GROUP BY clause, which allows you to group data based on one or more columns. Here are some real-life data examples to illustrate how to […]
ClickHouse is a powerful open source relational database management system that offers high performance, scalability, reliability and data security. As ClickHouse is widely used in various industries, it is important to ensure that it is performing optimally and meeting the needs of the business. To achieve this, ChistaDATA offers an Ultra-Low-Level Performance Audit service for […]
Derived tables are tables that are created on-the-fly as a result of a query. They are temporary tables that exist only for the duration of the query, and are not stored in the database. ClickHouse supports derived tables as a way to simplify complex queries and reduce the amount of data that needs to be […]
Permutation is the process of arranging a set of elements in all possible orders. ClickHouse supports two methods for computing permutations: recursion and cross join. Here’s how each method works, along with real-life data examples and use cases. 1. Permutation via Recursion: The recursion method involves recursively selecting each element in the set and computing […]
In ClickHouse, a SORT operation is used to sort data in a specified order, either ascending or descending. Sorting data can improve query performance when the data is accessed in a sorted order, such as when using an ORDER BY clause in a query. However, sorting large volumes of data can be resource-intensive and time-consuming. […]