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    Data Processing Overview

    Terakhir diperbarui:2024-12-20 16:13:10
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      Data processing
      provides capabilities for log data filtering, cleansing, data masking, enrichment, and distribution.
      According to the position of data processing in the data pipeline, and the different sources and sinks, the following data processing scenarios are currently supported:
      Scenario
      Description
      
      
      
      
      Log Collection - Processing - Log Topic:
      Logs are collected to CLS, processed (filtered, structured), and then written to the log topic. As shown, data processing occurs before the log topic in the data pipeline, referred to as preprocessing of data.
      Performing log filtering in preprocessing can effectively reduce log write traffic, index traffic, index storage, and log storage.
      Performing log structuring in preprocessing, with key-value indexing enabled, allows for SQL analysis of logs, dashboard configuration, and alarms.
      
      
      
      Log Topic - Processing - Fixed Log Topic:
      Store the data from the source log topic into a log topic after processing, or distribute logs to multiple log topics.
      
      
      
      Log Topic - Processing - Dynamic Log Topic:
      Based on the field value of the source log topic, dynamically create log topics and distribute related logs to the corresponding log topics. For example, if there is a field named Service in the source log topic with values like "Mysql", "Nginx", "LB", etc., CLS can automatically create log topics named Mysql, Nginx, LB, etc., and write related logs into these topics.

      Basic Concepts

      For terms and concepts related to data processing tasks, see Basic Concepts.

      Features

      Extract structured data to facilitate subsequent BI analysis, generation of monitoring charts (dashboard), etc. If your raw logs are not structured data, there is no way to perform SQL calculations, which means that OLAP analysis and CLS dashboard (drawing charts based on SQL results) cannot be performed on the logs. Therefore, it is recommended that you use data processing to convert unstructured data into structured data. If your logs are regular, you can also extract structured data at the time of log collection. See Full Regular Expression Format (Single Line) or Delimiter Format. Compared to the collection side, data processing offers more complex structured processing logic.
      Log filtering: Reduces subsequent usage costs. Discard unnecessary log data to save storage and traffic costs on the cloud. For example, you may deliver logs to Tencent Cloud COS and CKafka in the future, which can effectively save delivery traffic.
      Sensitive Data Masking: For example, masking information such as ID numbers and phone numbers.
      Log Distribution: For example, classifying logs according to log levels: ERROR, WARNING, and INFO, and then distributing them to different log topics.

      Advantages

      Easy to use, especially friendly to data analysts and operations engineers. Provides out-of-the-box functions with no need to purchase, configure, or maintain a Flink cluster. You can achieve stream processing of massive logs, including data cleaning and filtering, data masking, structuring, and distribution, by using our packaged DSL functions. For more information, see Function Overview.
      High-throughput real-time log data stream processing. High processing efficiency (millisecond latency) and high throughput, reaching 10-20 MB/s per partition (source log topic partition).

      Customer Cases

      Data cleaning and filtering: Client A discards invalid logs, retains only specified fields, and fills in some missing fields and values. If the log does not contain the fields product_name and sales_manager, it is considered invalid and discarded. Otherwise, the log is retained, keeping only the fields price, sales_amount, and discount. Other fields are dropped. If the log is missing the discount field, this field is added with a default value, such as "70%".
      Data Conversion: Client B has field values in the original logs that are IP addresses. The client needs to add fields and values for country and city based on the IP. For example, for 2X0.18X.51.X5, add the fields country: China and city: Beijing. Convert the UNIX timestamp to Beijing time, for example, 1675826327 to 2023/2/8 11:18:47.
      Log classification and delivery: Client C has original logs in multi-level JSON, which includes Array arrays. Client C uses data processing to extract the array from specified nodes in the multi-level JSON as field values. For example, extract the Auth field value from Array[0], and then distribute the log data based on the Auth field value. When the value is "SASL", deliver to target topic A; when the value is "Kerberos", deliver to target topic B; when the value is "SSL", deliver to target topic C.
      Log structuring: Client D processes the original log "2021-12-02 14:33:35.022 [1] INFO org.apache.Load - Response:status: 200, resp msg: OK" to complete structuring, resulting in log_time: 2021-12-02 14:33:35.022, loglevel: info, status: 200.
      For more detailed information, see Data Processing Cases.

      Fee Description

      Data processing incurs related costs. For details, see Billing Overview.
      If your business only needs processed logs, it is recommended to set the retention period of the source log topic to 3-7 days and disable the index of the source log topic to save costs effectively.

      Specifications and Limits

      For specifications and limitations of data processing, see Data Processing.
      
      
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