Semiotic Parsing Language (SPL) - Breakthrough DSL for IIoT Analytics - Part Two

Ashok Agarwal
Jun 13, 2018

In this section we will investigate how Glassbeam’s DSL called SPL (Semiotic Parsing Language) helps in parsing multi-structured machine logs.

SPL Terminology:


SPL allows a log file to be treated as a hierarchical document consisting of multiple segments (or sections). Each hierarchical segment is called namespace. This allows for zeroing in on the exact section to parse specific elements from, thus localizing the scope of extracts.

7 Must Have Items on Your Checklist Before You Consider Machine Data Analysis

Vijay Vasudevan
Jun 05, 2016

These are the 7 key factors that highlight whether performing machine data analysis realizes credible value for you.

1. Prepare the raw data —

Most of machine data are in the form of logs. Industrial machines are constantly producing valuable operational data (call-home data) on configuration, performance, usage, and other important parameters that define the very life of the device in the field.

Geting the most out of akka clusters

Aug 14, 2015

Anyone serious about distributed systems or building one, commonly encounters issues such as replication, consistency, availability and partition tolerance (CAP) [1]. In a real life scenario, partition tolerance is inevitable. So the system must be able to handle partition tolerance when there are network outages. Therefore, ‘P’ in the CAP is a must for any distributed system. This has been backed by Peter Deutsch in his (EIGHT FALLACIES OF DISTRIBUTED COMPUTING).

Elastically adding and removing nodes using akka cluster

Aug 07, 2015

This post explores a pull-based master/worker architecture – one that is suitable for anyone who is looking to elastically provision nodes when the load is higher than normal and under-provision when the load is below normal. In this post, the master accepts RSS links from frontend which can be a user submitting links and worker accept one RSS link and extract information such as article’s published date, title and a brief description. All this information is indexed into ELASTICSEARCH.

Welcoming dimension data to our family of customers

Jul 22, 2014

We’re thrilled to announce that Dimension Data has joined our fast-growing list of customers.

The Dimension Data deal is notable for a few reasons. It marks our foray into the Data Center Infrastructure management market – one that we think is primed for capitalizing on the various capabilities of our machine data analysis platform. And, that is exactly what Dimension Data is using Glassbeam for:

Our next meetup talks of big data technologies in glassbeam scalar

Jul 16, 2014

At Glassbeam, we are big fans of Functional Programming and Scala. SCALAR our hyper scale big data platform was built ground up using Scala. Additionally, we utilize the AKKA actor framework for asynchronous and distributed processing. We truly believe that SCALAR as an evolutionary platform for organizing and analyzing complex machine data in the era of the Internet of things (IOT). In case you missed it, here is the SCALAR launch ANNOUNCEMENT.

The intersection of machine data analytics and the internet of things

Sep 08, 2013

The ability to gather and harness data from machines and devices connected to the net is increasingly becoming a source of competitive advantage for those who have been thinking ahead. GE is widely cited, with their INDUSTRIAL INTERNET initiative. Cisco has been talking about INTERNET OF EVERYTHING for a while. At Glassbeam, we have been focusing on the infrastructure required to process and analyze machine data for over 3 years now.