A conversation with a storage executive last week brought up Gluster, a clustered file system I have not explored in many years. I had one interaction months before its acquisition by RedHat® in 2011.
I remembered the Gluster demo at Jaring over a video call, because I was the lead consultant pitching the scale-out NAS solution. It did not go well, and there were “bugs” which made the Head of IT flinched in her seat. Despite Jaring being Malaysia’s technology trailblazer, the impression of Gluster was forgettable. I stayed on the GlusterFS architecture a little while and then it dropped off my radar.
Gluster Scale Out NAS
But after the conversation last week, I am elated to revive my interest in Gluster, knowing that something big and impressive in coming into the fore very soon. Studying the architecture (again!), there are 2 parts of Gluster which excite me. One is the Brick and the other is the lack of a Metadata service.
[ Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies presented at this event. The content of this blog is of my own opinions and views ]
And the Emmy® goes to …
Yes, the Emmy® goes to Dell EMC Isilon! It was indeed a well deserved accolade and an honour!
This is not a blog to praise Isilon but one that instill respect to a real true clustered, scale-out file system. I have known of OneFS for a long time, but never really took the opportunity to really put my hands on it since 2006 (there is a story). So here is a look at history …
Back in early to mid-2000, there was a lot of talks about large scale NAS. There were several players in the nascent scaling NAS market. NetApp was the filer king, with several competitors such as Polyserve, Ibrix, Spinnaker, Panasas and the young upstart Isilon. There were also Procom, BlueArc and NetApp’s predecessor Auspex. By the second half of the 2000 decade, the market consolidated and most of these NAS players were acquired.
[ Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies presented at this event. The content of this blog is of my own opinions and views ]
Cloud computing will have challenges processing data at the outer reach of its tentacles. Edge Computing, as it melds with the Internet of Things (IoT), needs a different approach to data processing and data storage. Data generated at source has to be processed at source, to respond to the event or events which have happened. Cloud Computing, even with 5G networks, has latency that is not sufficient to how an autonomous vehicle react to pedestrians on the road at speed or how a sprinkler system is activated in a fire, or even a fraud detection system to signal money laundering activities as they occur.
Furthermore, not all sensors, devices, and IoT end-points are connected to the cloud at all times. To understand this new way of data processing and data storage, have a look at this video by Jay Kreps, CEO of Confluent for Kafka® to view this new perspective.
Data is continuously and infinitely generated at source, and this data has to be compiled, controlled and consolidated with nanosecond precision. At Storage Field Day 19, an interesting open source project, Pravega, was introduced to the delegates by DellEMC. Pravega is an open source storage framework for streaming data and is part of Project Nautilus.
The data generated at source (end-points, sensors, devices) is serialized, timestamped (as event occurs), continuous and infinite. These are the properties of a time series data stream, and to make sense of the streaming data, new data formats such as Avro, Parquet, Orc pepper the landscape along with the more mature JSON and XML, each with its own strengths and weaknesses.
You can learn more about these data formats in the 2 links below:
Many time series projects started as DIY projects in many organizations. And many of them are still DIY projects in production systems as well. They depend on tribal knowledge, and these databases are tied to an unmanaged storage which is not congruent to the properties of streaming data.
At the storage end, the technologies today still rely on the SAN and NAS protocols, and in recent years, S3, with object storage. Block, file and object storage introduce layers of abstraction which may not be a good fit for streaming data.
