Scaling data collection with Apache Flume and Python

Author: Zen and the Art of Computer Programming

1 Introduction

Apache Flume is an open source, distributed, and reliable log collector provided by Cloudera. It is widely used in scenarios such as logging, event collection, and streaming data transmission. It can process massive log data very well and can effectively ensure the integrity, real-time and timeliness of log data. Flume's data storage backend supports HDFS, HBase, Kafka and other storage systems. Flume can also perform preprocessing operations such as segmentation, compression, and encryption of data to further improve data security, availability, performance, etc. In this article, we will implement a simple log collection application through Python language to simulate the collection, aggregation and analysis process of log data in high concurrency scenarios. Since Flume and Python are closely integrated, this article is also an article about the integration of the two. I believe that by reading this article, readers can understand how to use Flume to aggregate data from multiple sources, and how to use Python to clean, calculate, and analyze the collected logs.

2. Explanation of basic concepts and terms

2.1 Introduction to Flume

Apache Flume (flume for short) is a distributed, reliable, fault-tolerant service for collecting, aggregating and moving large amounts of log files. Its main features include:

1. Reliability: Flume ensures that data will not be lost. Even if Flume itself or the log source fails, the integrity of the data will not be affected;

2. Data integrity: Flume uses a simple and easy-to-use transaction mechanism to ensure that data will not be destroyed or damaged;

3. Efficiency: Flume can batch process and quickly transmit logs, while having low latency, high throughput and high fault tolerance;

4. Supports multiple storage backends: Flume supports multiple storage backends, such as HDFS, HBase, Kakfa, etc. You can choose different storage backends according to your needs;

5. Support data preprocessing: Flume supports preprocessing operations such as data segmentation, compression, and encryption to further improve data security, availability, performance, etc.

2.2 Introduction to Python

Python is a free, open source, cross-platform computer programming language. Its design philosophy emphasizes code readability, simplicity, portability, and many other advantages. Python was originally developed by Guido van Rossum, and the first version was released in 1991. The latest version of Python is 3.7.0 as of July 2019. Python has a wealth of libraries and modules to support web development, scientific computing, data mining, machine learning and other fields, and has become a very popular programming language.

2.3 Log

Logs are text information that records the running status and processing flow of an application. It usually contains information such as timestamp, level, thread ID, category, message, etc. In actual production environments, logs are a very important source of information. In large software systems, a large amount of log data is often generated. In addition to helping developers locate problems, log data can also be used to do some statistics, monitoring and other tasks. Therefore, the technical knowledge and tools involved in log data collection, processing, analysis, search and reporting are also very important.

3. Core algorithm principles and specific operation steps

3.1 Log data model

A log data model refers to how log data is organized and structured to make it easy to retrieve, analyze, and understand. Generally, the log data model includes log name, fields, labels, categories, levels, etc. The following figure shows the most common log data model-the four-factor model:

In the four-element model, the four elements of the log are:

1. Log name : The log name refers to the name identifier of the log. Generally, the log name is generated by the application and indicates the system process or service to which the log belongs.

2. Log fields : Log fields refer to all relevant information in the log. It consists of different fields, each of which can have its own name, value and type. For example, a log may contain fields such as "User ID", "Access Time", "Request Parameters", etc.

3. Log tag : Log tag refers to additional information describing the log. It can be any information that facilitates log retrieval, analysis, and reporting, such as hostname, IP address, environment information, etc.

4. Log level : Log level refers to the classification of log importance. Generally speaking, log levels are divided into seven levels (from low to high): DEBUG, INFO, WARN, ERROR, FATAL, TRACE and ALL. Among them, DEBUG has the lowest level and FATAL has the highest level.

3.2 Flume basic configuration

Install

Flume can be installed through the source code package or by downloading the compiled binary package. For specific methods, please refer to the official website installation instructions.

Configuration

Flume's configuration file is named flume.conf and is stored in the /etc/flume directory by default. Flume configuration files contain three main parts:

1. agent
2. sources
3. channels
4. sinks

Agent: agent defines the basic properties of Flume, such as configuration check interval, whether it can be run independently, operation mode, etc. sources:sources defines the log data source. Flume supports a variety of data sources, including AvroSource, ExecSource, SpoolDirectorySource, etc. Here we only use ExecSource to simulate log collection. channels: Channels are one of the most important components in Flume, which are responsible for storing, caching and transmitting log data. It supports multiple buffering strategies, including memory, local disk, Thrift, MySQL, etc. Here we only use memory cache. sinks: sinks defines the log output destination. Flume supports a variety of output targets, including HDFS, Hive, LoggerSink, SolrJ, FileChannel, KafkaSink, etc. Here we only use LoggerSink to print logs to the console. Specific configuration examples are as follows:

#agent name
agent.name=quickstart-agent

#sources
source.exec = exec
 .command = tail -F /var/logs/*.log
 .filegroups = logs
 .batchSize = 1000
 .batchTimeout = 30 seconds

#channels
channel.memory.type = memory
channel.memory.capacity = 10000

#sinks
sink.console.type = logger
sink.console.loggerClassName = org.apache.flume.sink.ConsoleLogger
sink.console.logFormat = "%-4r %d{ISO8601} %-5p %c{1.}:%L - %m%n"

Among them, .commandis the execution command, specify tail -Fthe command to monitor /var/logs/*.logall log files in the folder. .filegroupsSpecify the log file group, there is only one group here logs. .batchSizeSet the batch transfer size, here it is set to 1000. .batchTimeoutSet the batch transfer timeout, here it is set to 30 seconds.

Then start Flume, the configuration file path is /etc/flume/flume.conf, and the command is flume-ng agent --config conf -f flume.conf -n quickstart-agent -Dflume.root.logger=INFO,console. Then wait for Flume to start normally, and you will see log data starting to be output to the console.

3.3 Data collection

Data collection is the most basic link in log data. Log data usually goes through the following stages:

1. Collection: read log files, pull logs from the log server, etc., and read the log data to the machine where Flume is located.

2. Split: If the log file is too large, you need to split the log file. Flume provides some plug-ins to complete log file segmentation, such as SpoolDirArchive and TaildirNewLineEscaped splitters.

3. Parsing: Flume supports multiple log format parsers, such as RegexParser, Log4jEventDeserializer, etc. After the log data is parsed, it will be stored in the memory cache according to the specified format.

4. Routing: Flume sends qualified log data to specified Channels by configuring filtering rules.

5. Consumption: When log data enters Channels, Flume will consume them in order and write them to the specified destination (such as HDFS, HBase, MySQL, etc.).

6. Cleaning: Flume supports a variety of log data cleaning methods, such as deleting special characters, IP address conversion, deduplication, etc.

Specific steps:

1. Read log data from the log source: You can use ExecSource or SpoolDirectorySource as the data source to write the log data to the Flume cache channel.

2. Parse and clean data: Flume supports multiple log format parsers, such as RegexParser, Log4jEventDeserializer, etc. You can also write custom parsers. At the same time, Flume supports a variety of log data cleaning methods, such as deleting special characters, IP address conversion, deduplication, etc.

3. Send data to Channels according to routing rules: Flume supports routing rules based on regular expressions, event types, timestamps, Hosts, etc.

4. Store data in Channels: Flume supports multiple types of Channels, including memory Channels, file Channels, database Channels, etc. Different types of Channels can be flexibly configured through configuration files.

5. Write data at the destination: Flume supports multiple destinations, including HDFS, HBase, MySQL, etc., and can be flexibly configured through configuration files.

3.4 Data processing

Data processing is the next step after log data collection. The main tasks are:

1. Data analysis: Analyze the log data cached by Flume according to business needs to form business-related indicators or data.

2. Log quality assessment: Evaluate the accuracy, completeness, operability and other dimensions of the log data cached by Flume, discover abnormal data and handle it accordingly.

3. Data report: Summarize, count, query and other operations on the log data cached by Flume to form a data report and present it to business-related personnel.

4. Data storage: Store the log data cached by Flume in various storage devices, such as HDFS, MySQL, etc.

5. Pipeline data processing: During the process of log collection, processing, and storage, a data processing pipeline can be designed to allow log data to be transferred between various links.

Specific steps:

1. Use big data processing frameworks such as MapReduce or Spark to analyze and process the log data cached by Flume.

2. Query Flume cached log data through SQL engines such as Hive, Impala, and Drill.

3. Use the HTTP Post Sink provided by Flume to synchronize data to external systems, such as Elasticsearch, Kafka, etc.

4. Use the Sqoop Source and Sqoop Connector provided by Flume to import data into the Hadoop cluster.

5. Use the JDBC Channel provided by Flume to import data into a relational database.

6. Use the JMS Sink provided by Flume to synchronize data to external systems.