Parseable v1.3.0 is out now! This release includes a good mix of new features, improvements, and bug fixes. In this post, we'll take a a detailed look at what’s new in this release.

We'll love to hear your feedback on this release. Please feel free to create an issue on our GitHub or join our Slack community.

New Features​

Saved filters and queries in the explore logs view​

A long-pending request from our community has been the ability to save a filter in order to return at a later date to a specific view without having to re-apply the filter from scratch. The same goes for queries.

We initially considered implementing this as a purely client-side feature, i.e. on the Console only, to deliver it more quickly. The idea was to use the browser's local store to keep a saved filter’s details and then load it from there on demand. But this approach would have been too limiting; for instance, the same user would not have been able to see their saved filters when logging in from a different browser or IP address. Also, sharing filters across users would not work and any browser event that cleared local storage would essentially mean the loss of all the saved filters, many of which are carefully created after months of analysis.

Temporal is a leading workflow orchestration platform. Temporal offers a robust, durable execution platform that offers guarantees on workflow execution, state management, and error handling.

One of the key aspect for a production grade application is the ability to reliably log and monitor the workflow execution. The log and event data can be used for debugging, auditing, custom behavior analysis, and more. Temporal applications are no different. Temporal provides logging capabilities, but it can be challenging to manage and analyze logs at scale.

In this post, we'll see how to extend the default Temporal logging to ship logs to a Parseable instance. By integrating Parseable with Temporal you can:

• Ingest workflows logs to create a centralized data repository.
• Co-relate events, errors, and activities across your workflows.
• Analyze and query logs in Parseable for debugging and monitoring.
• Setup reliable audit trails for your workflows using Parseable.
• Setup alerts and notifications in Parseable for critical events in your workflows.

and much more.

Behavioral data analysis examines detailed user activity logs to understand customer behavior on a website. Companies that utilize this tactic have a competitive advantage in their industry.

This article will explore how to analyze clickstream data generated from an eCommerce portal and use it to understand user preferences visitor traffic session information and more by building a report and dashboard using Parseable and Grafana.

Managing and deriving insights from vast amounts of historical data is not just a challenge but a necessity. Imagine your team grappling with numerous log files, trying to pinpoint issues. But, because logs are stored as files, it is very inefficient to search through them. This scenario is all too familiar for many developers.

Enter Parseable, a powerful solution to analyze your application logs. By integrating with pandas, the renowned Python library for data analysis, Parseable offers a seamless way to ingest and leverage historical data without the need to discard valuable logs.

In this blog post, we explore how Parseable can revolutionize your data management strategy, enabling you to unlock actionable insights from both current and archived log data effortlessly.

Boomi is a leading integration Platform as a Service (iPaaS) that allows you to design, deploy, and manage integration processes. However, managing logs can be challenging. Using Parseable with Boomi reduces the headache and enables the user to experience a seamless process.

Boomi logs are a goldmine of information, helping users get the information they need, yet there are significant challenges in accessing and interpreting the logs.

Traditionally, these logs are stored in files, and extracting meaningful insights often involves manual efforts that are both time-consuming and error-prone.

In this article, learn how to use Parseable with Boomi to utilize its full potential.

Salesforce is a highly customizable and widely used CRM platform that uses observability to maintain and troubleshoot its applications. Observability in Salesforce primarily involves monitoring logs, setting up alerts, and tracking performance metrics.

The problem is that we need more of the right insights, and the leading cause of this is the app's inflexibility. This is nothing new, but how do we fix the problem?

Using an open source, flexible, and extensible tool like Parseable, along with Factorwise for Salesforce integration - you can improve observability in your Salesforce application for optimal health and performance, helping you serve customers and generate revenue. Let's see how.

Top 4 limitations for Observability in Salesforce​

Salesforce offers built-in debugging tools like debug logs dump for tracking events. These are written in Apex, or low-code features like flow. However, this has significant limitations:

• Real-time parsing challenges: Debug logs are not designed for reading in real-time, making it difficult for developers to identify and resolve issues quickly.
• Developer console limitations: While the Developer Console can search through logs, it is not user-friendly and can be unstable, especially with large log files. Its session-based nature can lead to frequent crashes.
• Performance impact: Enabling Debug logs in production environments can degrade performance, creating a trade-off between observability and system efficiency.
• Low-level detail overload: Debug logs often contain low-level details that may not be relevant to developers, who typically look for application-level logs such as System.debug calls.

How we've previously addressed these challenges​

Third-party services​

Several third-party services have tried to address Salesforce's observability challenges in the past. However, these solutions are often not viable for all Salesforce operations teams, particularly those in medium and small businesses, due to cost constraints. As a result, many teams continue to rely on the built-in debug logs despite their limitations.

Another constraint they encounter is the need for user-friendly interfaces for log visualization and searching.

Custom tooling​

Some organizations develop custom logging solutions to overcome these challenges, but they often involve storing logs within the Salesforce instance and publishing them on the Event Bus. An example of this is NebulaLogger. While effective to some extent, without a helpful user interface for log visualization and search capabilities, insights cannot be understood, evaluated, and acted upon in a way that improves observability in Salesforce. Additionally, Salesforce's native search on large text areas can be slow, hampering efficiency.

How we address Observability in Salesforce today with Parseable & Factorwise​

Parseable is an open-source log analytics tool that simplifies and enables bold insights for teams looking for a competitive edge.

Intuitive dashboard for log management​

Parseable's intuitive UI and familiar SQL syntax make it simple yet powerful for developers to query logs.

Factorwise Logger, generates unique transaction IDs for every invocation of custom Salesforce logic, simplifying the process of tracking and debugging specific transactions.

With Parseable, Salesforce logs can be parsed and searched in real time, enabling rapid identification and resolution of issues. This capability is a significant improvement over the traditional Salesforce Developer Console, providing a more stable and user-friendly interface for log management.

The right alert for the right team at the right time​

Parseable enables real-time alerts based on defined conditions, ensuring that abnormal events are promptly identified and addressed. Notifications are delivered to the right team via Slack, Alertmanager, or custom webhooks, keeping the team or person you choose informed and able to respond sooner rather than later to potential issues.

Factorwise Logger captures Salesforce governor limits on every log event generated, storing this data in Parseable. This feature enables teams to track performance bottlenecks and optimize their applications effectively.

Visualize metrics that matter to your team​

Parseable also supports the aggregation and visualization of business and operational metrics!

For example, teams can track the number of leads converted over various time intervals, gaining insights that are difficult to obtain using native Salesforce reports. Parseable's fast query results make real-time data analysis straightforward and efficient.

For Grafana fans and users, Parseable integrates seamlessly with this popular platform for monitoring and observability to create real-time dashboards that provide a comprehensive view of system performance and business KPIs, enabling data-driven decision-making.

Parseable x Factorwise is ideal for Salesforce log analytics​

Factorwise is building a Salesforce native logger, that integrates very well with Parseable. This logger is built with detailed insights from 100s of Salesforce users. Meanwhile, Parseable stands out as the optimal choice for Salesforce observability for several reasons:

• Ease of use: Parseable is easy to deploy, operate, and scale, making it accessible for organizations of all sizes. Its intuitive UI and SQL-based query language reduce the learning curve and enable rapid adoption.
• Comprehensive features: Parseable offers a range of built-in features, including real-time log parsing, alerts, RBAC (Role-Based Access Control), and soft-multi tenancy through stream-based segregation. These capabilities ensure that teams have everything they need to maintain robust observability.
• Cost-effectiveness: For medium and small businesses, Parseable provides a cost-effective solution that doesn't require extensive resources to manage. This makes it an attractive alternative to more expensive third-party services.

If you'd like to talk about how Parseable x Factorwise can help your Salesforce team, please schedule a demo.

We'd love for you to try Parseable today! Ask questions and tell us what you think by joining our community in Slack.

If you are a programmer, you might have come across various mainstream practices, one of them being logging. The process itself can act as your silent guardian and helps you record the changes and steps your application is taking. While there is no standardized rule book for you to refer, we have compiled a few rules of thumbs to start your journey with. Effective logging isn’t just about collecting data, it involves a well-defined strategy. Consider this blog post as your beginner’s guide to logging practices.

Clickstream data are a series of individual events. These individual events are sequenced to do analytics and draw a variety of insights. To build a dependable analytics system, you need to build reliable infrastructure and a pipeline to record each event. You can build a robust clickstream data infrastructure using React, Node.js, Winston, and Parseable.

With the release of Parseable v1.2.0, we are excited to introduce a major upgrade focusing on enhanced querying and ingestion capabilities. This new version brings a suite of powerful features designed to improve the speed, flexibility, and compatibility of log management.

Our focus this release has been to improve user experience when querying data. Additionally, we made several server side improvement to enhance the query performance.Let's go over the highlights of this release.

Clickstream consists of a series of ongoing user events (such as page visits, button clicks, form submissions, etc.) on websites or apps. These events capture a wealth of information about user behavior and site usability. Data-driven teams log these events, query, and sequence them to gain insights. Teams from marketing, product, and UI/UX departments use this data as a secret sauce to build delightful products.