It seems that everyone is more interested in interviews. I will share some real interview questions with you one after another, but I will mainly share them in a series. After all, I want to recruit people who are actually working. Contribution, isn't it proper that stereotypes are not required?
1. Why is ClickHouse so fast?
The reason why ClickHouse is so fast is that it uses a variety of technologies and optimization strategies in its design and implementation:
1. Columnar storage: ClickHouse uses columnar storage, which can improve data compression ratio and reduce I/O access. 2.
Data partitioning: ClickHouse supports dividing data into different partitions, which can reduce the scope of data scanning and improve query speed 3.
Data localization: ClickHouse can store data on local disks, avoiding data network transmission Overhead
4. Data compression: ClickHouse supports a variety of data compression algorithms, which can reduce the amount of disk I/O access and network transmission data 5.
Vectorized computing: ClickHouse uses SIMD instruction set and CPU cache to implement vectorized computing, which can be used in Improve calculation speed when processing large data sets
6. Parallel query: ClickHouse supports parallel query, which can divide a query into multiple sub-queries and execute them simultaneously, thus speeding up query speed 7.
Multi-level cache: ClickHouse supports multi-level cache, which can store hot data Stored in memory to reduce disk I/O access
In summary, ClickHouse uses a variety of optimization technologies, such as columnar storage, data partitioning, data localization, data compression, vectorized computing, parallel query, and multi-level caching, etc. makes it have excellent query performance and scalability
Follow-up: How to understand data localization?
"Data locality" refers to storing data on local disks rather than distributed across multiple nodes. ClickHouse supports localized storage, which means that users can store data on the local disk of a single node instead of distributed storage on multiple nodes
Follow-up: Briefly introduce the ClickHouse data compression algorithm?
1. LZ4 compression algorithm: LZ4 is a lossless compression algorithm, which can quickly compress and decompress data, and has a high compression ratio. In ClickHouse, the LZ4 compression algorithm is often used to compress dictionary-type data, such as IP addresses and city names.
2. Zstandard compression algorithm: Zstandard is a lossless compression algorithm with fast compression speed and high compression ratio. In ClickHouse, the Zstandard compression algorithm is often used to compress non-dictionary data such as text, JSON, and CSV
2. Why is ClickHouse faster than ES
ClickHouse and Elasticsearch (ES) are two different data storage and processing systems, and their designs and application scenarios are also very different. Although they can all be used for data analysis and search, their performance characteristics are different:
1. Storage structure: ClickHouse adopts columnar storage structure, which can improve data compression ratio, reduce I/O access, and thus speed up query speed. ES adopts a document-style storage structure, which has good support for real-time indexing and searching, but the performance will be affected to a certain extent during large-scale data query and aggregation calculations. 2. Data processing method: ClickHouse adopts vectorized calculation method
, It can improve the calculation speed when processing large data sets, while ES adopts iterative calculation method and has good support for real-time indexing and searching, but the performance will be slower in large-scale data query and aggregation calculation. 3. Query optimization
: ClickHouse has many excellent algorithms and technologies in query optimization, such as adaptive indexing, data partitioning, multi-level caching, etc., which can effectively improve query performance. However, ES has relatively little optimization in this area.
4. Data reliability: ClickHouse adopts a multi-copy data backup mechanism to ensure data reliability and fault tolerance. The data backup mechanism of ES is relatively weak
Generally speaking, ClickHouse and ES have their own advantages and disadvantages, and which system to choose mainly depends on the application scenarios and requirements. If you need to perform large-scale data query and aggregation calculations, or need to ensure data reliability and fault tolerance, you can choose ClickHouse; if you need real-time indexing and search, or need to support advanced search functions such as text analysis and aggregation, you can choose ES
Follow-up: A brief introduction to adaptive indexing
Adaptive indexing is an indexing technology that automatically adjusts indexing strategies based on query patterns. Different from traditional manual index creation and maintenance, adaptive index can dynamically select the appropriate index strategy according to the data access mode, thereby improving query performance and reducing storage overhead.
Adaptive indexing usually uses statistical analysis methods to analyze query patterns and data distribution to select the most suitable indexing strategy. For example, you can create multiple indexes based on frequent query conditions, or create partitioned indexes based on data distribution. Adaptive indexing can also automatically adjust indexing strategies when data access patterns change to accommodate new query requirements.
Adaptive indexing is usually applied to large-scale data storage systems such as columnar storage databases. Due to the huge amount of data and high query complexity, manually creating and maintaining indexes is often a time-consuming and labor-intensive task. By using adaptive index technology, you can reduce index maintenance costs and improve query performance and efficiency
3. Disadvantages of columnar storage
1. Low update operation performance: Since data is stored in different columns, multiple columns need to be updated for each update operation, which results in lower write performance than row-based storage.
2. High concurrency performance problem: When multiple query requests need to read different columns at the same time, because the data is stored in different columns, multiple I/O operations may be required, which will cause high concurrency performance problems.
3. Not suitable for real-time query: Since columnar storage requires scanning and aggregation calculations on multiple columns, there may be certain limitations in real-time query.
4. Not suitable for small-scale data sets: Since the advantage of columnar storage lies in the processing of large-scale data sets, problems such as waste of storage space and poor query performance may occur when processing small-scale data sets.
In general, columnar storage is suitable for the analysis and query of large-scale data sets, but it is also necessary to choose an appropriate data storage method according to specific application scenarios and requirements. For scenarios such as frequent updates, high concurrency requirements, and real-time queries, other storage methods may need to be
considered