Application practice of Redis in crawler data processing

Application practice of Redis in crawler data processing

With the development of the Internet, crawler technology has gradually been widely used. However, in large-scale crawler tasks, data processing and storage is a huge challenge. Traditional database storage methods are difficult to meet the requirements of high concurrency, high availability, and high performance. As a high-performance, memory-based database, Redis is used by more and more crawler developers.

This article will introduce the application practice of Redis in crawler data processing. This will be a very valuable reference for crawler developers.

1. Redis data structure

Redis supports a variety of data structures, including strings, hash tables, lists, sets, ordered sets, etc. These data structures are characterized by very fast read and write speeds, making it easy to implement efficient data processing.

In the crawler, we can distinguish data according to type and store it in different Redis data structures. For example:

1. String

String is the simplest data structure of Redis and can store any type of data. In the crawler, we can store some commonly used temporary data (such as proxy IP, request headers, cookies, etc.) into strings and read and write them through key-value pairs.

2. Hash table

The hash table is another commonly used data structure in Redis, which consists of multiple key-value pairs. In the crawler, we can classify the data according to websites or keywords and store it using a hash table. For example:

hset website1 url1 content1
hset website1 url2 content2

hset website2 url1 content1
hset website2 url2 content2

In this way, when querying the specific URL of a specific website, you can quickly find the content of the URL through the hget command of Redis.

3. Lists and Sets

Lists and sets are also commonly used data structures in Redis. List elements can be repeated, but set elements cannot be repeated. In the crawler, we can store the URL collection in the Redis Set data structure. At the same time, the visited URLs can also be stored in the Redis list structure, so as to avoid repeated visits to the visited URLs.

2. The actual application of Redis in crawlers

1. Storage proxy IP

In crawlers, in order to avoid being recognized and banned by the website, We usually use proxy IP for access. In order to improve crawler efficiency, we hope to quickly obtain idle IPs from the proxy IP pool. At this time, we can use the List data structure of Redis to store the proxy IP in the list, and use the Redis command rpoplpush to move the idle IP from the head to the tail of the list. When the crawler needs to use a proxy IP, it only needs to pop an IP from the end of the list.

2. Storing crawling results

In the crawler, we need to store the crawled data. Usually, we will choose to store data in a relational database (such as MySQL). However, an important problem faced by this solution is the database performance problem under high concurrency and high read and write pressure. As an in-memory database, Redis can ensure high-speed reading and writing speeds and high concurrency capabilities.

For example, when crawling data such as papers, we can first store the paper title, author and other information through the Redis hash table. Then, the main text of the paper is stored using Redis's string data structure. This makes it easy to search for papers and greatly improves reading and writing efficiency.

3. Storing crawler task status

In high concurrency situations, crawlers may encounter task duplication, unexpected interruptions, etc. In this case, we need to record the status of each crawler task to ensure data consistency. For example, in the crawler task, we can store error information, status information, etc. during the collection process through the Redis hash table. When the crawler task is restored or restarted, you only need to obtain the last task status from the Redis hash table to continue collecting.

3. Thinking

1. Limitations of Redis application

Compared with traditional relational databases, Redis has advantages in data persistence, complex queries, etc. There are certain deficiencies in this aspect. Therefore, when choosing Redis as a tool for crawler data processing and storage, it needs to be measured based on the actual situation.

2. The combination of Redis and distributed crawlers

Redis is often used in distributed crawler systems, working with tools such as celery and scrapy for task distribution, state sharing and other operations. When using Redis for data processing, you need to pay attention to data synchronization issues to avoid data conflicts and inconsistencies.

4. Conclusion

As an in-memory database, Redis has shown very superior performance in crawler data processing and storage. By using Redis' different data structures, we can quickly store, read, and find data. At the same time, Redis can also be integrated with other distributed crawler tools to improve the overall performance and stability of the crawler system.

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