MongoDB is a popular NoSQL database as it is a highly scalable and cost-effective one. It is a document-oriented and cross-platform database application.
The differentiating factor of a NoSQL database is that it stores data differently from relational databases like MySQL and is not tabular.
MongoDB uses JSON-like documents with optional schemas. The data can be used and stored without worrying about relationships or tables. And for diagnosing issues, monitoring your deployment, and fine tuning performance, MongoDB Logs are useful.
Big businesses widely use MongoDB, and it’s understandable why. MongoDB is quicker than a SQL database and is simple to scale. It is a fantastic choice if you’re searching for a database that won’t have you spend time worrying about relationships and scalability.
It is a top-tier, C++-written open-source NoSQL database. Another factor in choosing it is that it’s free and performs better. In this blog, you can learn more about how to analyze MongoDB logs messages using the free MongoDB logs analyzer tools like mtools.
Table of Contents
- What is MongoDB, & How Does It Store Data?
- What Are MongoDB Logs?
- What Types of Event Messages Should You Monitor in MongoDB Logs?
- What are MongoDB Logs Message?
- What are mtools?
- Visualizing MongoDB Logs File with mplotqueries
What is MongoDB, & How Does It Store Data?
Technically speaking, MongoDB is a document-oriented NoSQL database, meaning that documents are used in a schema to describe models. MongoDB stores documents (objects) in a format called BSON. In addition, its primary selling factors include simple indexing, replication, ad-hoc queries, schema lessness, cross-platform support, and superior performance.
Consider a document an object whose keys and values fall under the same taxonomy. Here is an example of a JSON-encoded document model for a typical user and its corresponding BSON:
Here is a JSON document example.
"hello” : "world"
MongoDB is innovative by incorporating this structure into a new format known as BSON, a binary representation of JSON data optimizing the speed, schema flexibility, and governance, resulting in enhanced scalability.
When storing the JSON document, it will be converted to the following.
\x16\x00\x00\x00 // total document size
\x02 // 0x02 = type String
hello\x00 // field name
\x06\x00\x00\x00world\x00 // field value (size of value, value, null terminator
\x00 // 0x00 = type EOO ('end of object')
MongoDB partitions data by breaking up more enormous shards into smaller pieces which can be performed automatically or manually, depending on certain conditions.
What Are MongoDB Logs?
When you use MongoDB, it records some messages in its logs, just like any other database, including MySQL.
It is helpful to be aware of the issue before it arises and causes any negative impact. You can fix the problem in advance if you are aware of it. You can find information in the logs that, when used properly, can save time and effort in case of issues.
As the ability of businesses to collect data explodes, data teams have a crucial role in fueling data-driven decisions. Yet, they struggle to consolidate the data scattered across sources into their warehouse to build a single source of truth. Broken pipelines, data quality issues, bugs and errors, and lack of control and visibility over the data flow make data integration a nightmare.
1000+ data teams rely on Hevo’s Data Pipeline Platform to integrate data from over 150+ sources in a matter of minutes. Billions of data events from sources as varied as SaaS apps, Databases, File Storage and Streaming sources can be replicated in near real-time with Hevo’s fault-tolerant architecture. What’s more – Hevo puts complete control in the hands of data teams with intuitive dashboards for pipeline monitoring, auto-schema management, and custom ingestion/loading schedules.
This, combined with Hevo’s pricing and 24×7 support, makes us the most loved data pipeline software on review sites.
Take our 14-day free trial to experience a better way to manage data pipelines.
Get started for Free with Hevo!
What Types of Event Messages Should You Monitor in MongoDB Logs?
MongoDB encodes numerous log messages. You will not use every log message to address your application’s issue. You must know what information from MongoDB logs is crucial.
You’ll primarily search for log messages like fatal, error, warning, and debug.
According to the official MongoDB documentation, the log messages have various levels ranging from fatal to debug, where debug is the lowest level. Beginning with MongoDB 4.4, all log messages are output by mongod/mongos instances in structured JSON format.
The log entry format is a set of key-value pairs, where each key denotes a field type of the log message, like “severity,” and each corresponding value contains the logging data related to that field type, like “informational.”
What are MongoDB Logs Message?
You can find MongoDB logs in the MongoDB log files at /var/log/mongodb/mongodb.log or else; check the mongodb.conf.- a configuration file specifying the location of logs.
The log file structure is the first thing you’ll need to understand. The log file is a JSON structure, as described earlier. Each key in a key-value combination that makes up a log entry identifies a different sort of log message field, such as “severity.”
For each appropriate value, such as “informational,” the accompanying logging data gets recorded. Log entries appear as plain text, which isn’t always readable.
What are mtools?
You can use Unix commands as a MongoDB log analyzer to do a fair analysis. However, several tools make log analysis more accessible. mtools is a fantastic open-source utility.
Suppose that slow queries executing against MongoDB are hurting the performance of the database; you can determine where MongoDB is slowing down by using mtools. mtools is a collection of Python utility scripts to analyze and filter MongoDB log files, display information from log files, and rapidly build up sophisticated local MongoDB test setups. Look at the “queries” section of mloginfo first.
Use command – mloginfo mongod.log –queries
Each line shows the namespace, the query pattern, and statistics of a particular namespace/pattern combination. The rows get sorted by the “sum” column, descending. Sorting by sum is an excellent way to see where the database spent most of its time.
Further mlogfilter assists in refining log file searches. The script allows you to filter log messages based on their namespace (database and collection names), operation (queries, inserts, updates, commands, etc.), and individual connection.
Lastly, mplotqueries takes a log file (mlogfiltered or not) and visualizes the data in various ways. Several graph kinds are available, including scatter plots (showing all operations over time versus their length), histograms, event and range plots, and other specific graphs such as connection churn and replica set changes.
Visualizing MongoDB Logs File with mplotqueries
mtools can provide visualization of query performance and other operations graphically. mplotqueries’ scatter plot shows the duration of any operation (y-axis) over time (x-axis), thus making it easy to spot long-running operations. The following plot gets generated with
and then press L for the “logarithmic” y-axis view:
The blue dots stand out, showing hundreds and thousands of seconds, unlike others which are sub-seconds—clicking on any of the blue dots prints out the relevant log line to stdout for analysis and action. You can learn further in-depth details on mtools here.
Similar to other log messages, MongoDB logs are pretty helpful for resolving problems. You may use the terminal to analyze the log file by displaying its contents. Even though this is functional, it is difficult to view or search data through the terminal. It becomes considerably worse when the log file contains important data.
The commands included in mtools MongoDB log analyzers, such as mloginfo, mlogfilter, mplotqueries, mlogvis, and mlaunch, may help you do more with a tool like mtools. This program has several limitations, including the inability to show data on the terminal, despite its vast command set. You may also explore commercial mongodb log analyzer tools for MongoDB log analysis if required.
VISIT OUR WEBSITE TO EXPLORE HEVO
Hevo, a No-code Data Pipeline, provides you with a consistent and reliable solution for seamless data replication from a wide variety of Sources & Destinations — that, too, in just a few clicks!
Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also look at the amazing Hevo’s price, which will assist you while selecting the best plan for your requirements.
Share your experience understanding MongoDB Logs and how we analyze them in the comment section below! We would love to hear your thoughts.