With a growing user base, the range of log sources DevOps needs to monitor also grows. In such cases, a centralized log management solution greatly helps. While choosing a log management solution, it’s crucial to focus on advanced aggregation capabilities of tools designed to allow grouping of logs, based on various characteristics such as log origin, user action, error type, and more. Intelligent pattern recognition is another important feature organizations should look for. It uses advanced machine learning abilities to compare new events to the old ones which helps in the determination of relevant events. Flagging, GitHub integrations, deep storage, and search are few other functionalities simplifying the logging tasks. Outlined below are the innovative logging solutions and their detailed feature analysis.
Loggly
SolarWinds® Loggly® is a cloud-based SaaS solution for log data management. With this tool, users can easily aggregate logs from the entire infrastructure and bring them together in one place to track activity and analyze trends. Loggly can be used by different teams, such as development, technical operations, product management, and customer services. The tool serves multiple purposes such as deployment monitoring, application analytics, troubleshooting server and application issues, transaction correlation, and alerting.
In addition, it offers high-level security. It helps secure the log data either over syslog TLS or HTTPS. The PRO and Enterprise level versions of Loggly offer long-term archiving/log data storage to Amazon Web Services S3 buckets. This allows users to access logs even if the plan has exceeded the retention period. The tool offers various advanced features such as dynamic field explorer, automatic alerts, default and custom dashboards, and derived log fields.
Sumo Logic
Sumo Logic is a continuous intelligence platform used by organizations to collaborate, operate, develop, and secure applications. It’s a cloud-based machine data analytics platform designed to proactively identify performance issues, help ensure seamless device availability, and enhance application rollouts. The tool helps to centralize large volumes of AWS logs and provides visibility into Azure, AWS, and GCP cloud applications in real time. Moreover, it comes with data visualization capabilities to translate events clearly and bring more visibility to every component of the stack. Sumo Logic is a highly scalable log management solution streamlining massive workload migrations with growing needs. It also includes built-in pattern detection, predictive analytics, and anomaly detection.
Loggly vs. Sumo Logic
The significant difference between both the tools is Loggly is a dedicated centralized log management tool with SIEM capabilities. In contrast, Sumo logic is a cloud-based machine data analytics platform to identify application threats and performance issues. Although both the tools are easy and simple, they differ in their offerings, features, and abilities such as search, filtering options, live event streaming, parsing, and backup. Outlined below are the key differences between the two innovative logging solutions.
Unified Logging
Unified logging captures data from the remote and inaccessible points for monitoring and analysis. It helps improve service quality and reduce mean time to resolution (MTTR).
Both Loggly and Sumo Logic offer unified logging features for improved availability and performance of the applications. Loggly combines unified logging with deep analysis capabilities, whereas Sumo Logic uses predictive analysis to identify root cause and trends, and resolve issues.
Loggly has redesigned the charts and dashboards to connect the dots and provide in-depth insights into log data with multiple visualization options. Teams can create and share dynamic charts and dashboards, contextually correlate metrics and logs, connect insights from dashboards to source code in GitHub, and examine timeshift charts. Sumo Logic, with its unified logging capability, allows users to view log dashboards together in one place.
Monitoring Machine Learning Models
Machine learning helps systems to automatically learn and perform tasks from past experiences without being programmed. Despite advanced automation capabilities, machine learning models need to be monitored on a timely basis to avoid disruptions and ensure their smooth functionality.
Loggly is capable of monitoring machine learning models designed in Amazon SageMaker (a machine learning framework) with the help of Amazon CloudWatch Metrics integration. SageMaker helps organizations in efficient extraction and analysis of data stored in Amazon S3 buckets, improving model accuracy, and input data quality.
Sumo Logic CloudWatch Source, on the other hand, only supports CloudWatch metrics. It’s not capable of ingesting metrics emitted with significant latency or at sporadic intervals, such as AWS S3 Daily Storage Metrics and AWS DynamoDB throttled events.
Indexing and Searching
Both the tools use Boolean search syntax, which means users can combine keywords with modifiers such as AND, NOT, and more to provide relevant results. When it comes to complex indexing and searching, Loggly is quick and efficient. It offers comprehensive and robust search features. According to search engine lingo, there are different types of searches, such as dense, sparse, fresh, repeated, hot, cold, statistics, and text impacting performance. Loggly includes several custom-built features to help ensure improved search results, irrespective of the type of search it’s performing. It parses the data before indexing, so it becomes easier and faster to search specific fields. The tool also provides an extra search caching to get results for repeated searches quickly.
Overused indexing can slow down Sumo Logic search results. The tool needs to process all the indexed data, which can make the search process long and time-consuming. Although the tool uses partitions to improve search performance, it has certain limitations. Too many partitions lead to fragmentation and data management issues. Therefore, it’s recommended to use a maximum of 20 partitions. Overlapping partitions leads to duplication of data and eventually, degrades its performance.
Conclusion
Choosing any one log management solution can be daunting. Both the tools are well equipped with log monitoring and analysis features. However, with the above-highlighted feature analysis, Loggly isn’t only a centralized log management solution but also a true Sumo logic alternative. It includes advanced logging capabilities such as catching exceptions, unified logging, tracking execution flow, anomaly detection, and updated dashboards and reports for immediate log data visualization. Both the tools offer free trial versions with a fair idea of their features and functionality.
*As of June 2020