Alcatel-Lucent is a French company. Our group in particular is a network management group. We provide network management software solutions to ALU customers. When a customer buys ALU’s network hardware, they would then require a software management tool to manage and operate the network, monitor its efficiency, and provide necessary information needed to support informed decision-making.
Cassandra’s story started at ALU close to two years ago. Now, I know at least three, or four, other teams inside ALU using it, or at least considering its use. There is a lot of data flowing in a network. ALU customers want to make use of this data. With Cassandra, that’s now possible and it’s scalable. Interactions between teams are now beginning to emerge and this will allow everyone to explore ways to maximize the potential. ALU is trying to build up a solid Cassandra foundation for the whole network management solution.
At this stage we are concerned with network stats collection. ALU’s network management solution initially collected and served the data with Oracle. While it stayed with Oracle for a long time, Oracle just couldn’t scale up at a reasonable cost. We had to look at other solutions that would make our solution cost-appealing to our customer.
During this journey, there’s been a lot of learning, a tremendous amount of information and also a complete mind-shift on how we think of the network data. We really had to switch the way we looked at things before. It was when we realized we needed to change the way we perceive data and legacy approaches that we realized what Cassandra can offer to us in terms of scalability.
ALU did its homework and looked at different options, Cassandra being one of them. We evaluated solutions like: HBase, MongoDB, and other similar NoSQL solutions, and we even looked at Oracle to see what they could offer in that domain.
A lot of things led us to decide to go with Cassandra. We needed a solution to scale up and down to properly support our small and big customers and their corresponding needs. Other things we like and we couldn’t get with Oracle, was the throughput; the amount of data we could push through, and quickly able to get out. Those were key factors. As well, how easy it was for Cassandra to maintain those rivers of data we are getting from the network and how the old unneeded data was aging out and disappearing without us worrying about it.
Another consideration was the uniqueness of our use case because we embed Cassandra as part of our product and then ship it to customers. So, we needed to be able to maintain many different use cases by many different customers with the same exact solution. As such we had to be able to provide a solution that will cater to all of them at the same time, which I think makes our relation to Cassandra different than a lot of other companies. Such flexibility of Cassandra was also a key factor.
ALU is using the community version of Cassandra right now, we start with our customers with a three to five nodes cluster. That’s just a starting point; bigger customers, like tier 1 operators, would definitely need more than this, and we are able to change that at a moments notice. The customer has the ability to add in the cluster to the solution; moving away from Oracle and add nodes so they can just simply scale. They can easily say, “I want to scale now”, we add additional nodes, and they will be able to instantly meet their load demands. We also help by giving them tools to figure out when it is necessary to do so.
I think adding Apache Spark could be the next step for ALU; our focus in this stage, is to figure out how to get the data in, and to be able to get it out in the most traditional ways that we and our customers are used to. Because we have this data structured in a different way with Cassandra, we have more ideas on how to get it out, and visualize it.
CQL at first glance, it looks like you can use similar concepts to SQL and relational databases, build tables with primary keys ..etc. In the beginning, we were very disappointed that there is a little support for relational concepts such as joins and referential integrity and we first thought, “how can that be possible!!”. Then, over time, we learned that it’s even better to do it this way, the structure of the data is much tighter; even with performance aside, it’s much better to do it this way. Cassandra has all been a learning exercise for us, we are very happy about the end result, but there’s still a lot of homework to do.
The value Cassandra adds is definitely for ALU and its customers as well. It’s a very shared value. They buy our products, our professional services, because they want their networks to efficiently operate and scale. What we do, and Cassandra helps us with, is we give them the backbone that will make it possible.