This might be a controversial topic, but I am concerned about the performance of boost graph vs commercial software such as TigerGraph, since we need to choose one.
I am inclined to choose Boost, but I am concerned whether performance-wise, boost is good enough.
Disregarding anything around persistence and management, I am concerned with boost graph's core performance of algorithms.
If it is good enough, we can build our application logic on top of it without worry.
Also, I got below benchmarks of LDBC SOCIAL NETWORK BENCHMARK.
LDBC benchmark
seems that TuGraph is the fastest...
Is LDBC's benchmark authoritative in the realm of graph analysis software?
Thank you
I would say that any benchmark request is a controversial topic as they tend to represent a singular workload, which may or may not be representative of your workload. Additionally, performance is only one of the aspects you should look at as each option is built to target different workloads and offers different features:
Boost is a library, not a database, so anything around persistence and management would fall on the application to manage.
TigerGraph is an analytics platform that is focused on running real-time graph analytics, such as deep link analysis.
Amazon Neptune is a fully managed service focused on highly concurrent transactional graph workloads.
All three have strong capabilities and will perform well when used in the manner intended. I'd suggest you figure out which option best matches the type of workload you are looking to run, the type of support you need, and the amount of operational work you are willing to onboard to make the choice more straightforward.
Related
I realize that this might not be the best platform to ask this, but I think this would be best unbiased one to put my question in.
How would you compare OpenMDAO v/s modeFrontier with regards to there optimization capabilities and application scaling and overall software development? Which one would you pick and why?
If you know of any resources or link do provide.
The most fundamental technical difference is OpenMDAO can pass data + derivative information between components. This means that if you want to use gradient based optimization and have access to at least some tools that provide derivative information, OpenMDAO will have far more effective overall capabilities. This is especially important when doing optimization with high-cost analysis tools (e.g. partial differential equation solvers --- CFD, FEA). In those situations making use of derivatives offers between a 100x and 10000x speedup.
One other difference is that OpenMDAO is designed to run natively on a distributed memory compute cluster. Industrial frameworks can submit jobs to remote clusters and query for the results, but OpenMDAO itself can run on the cluster and has a direct and internal MPI based distributed memory capability. This is critical to it being able to efficiently handle derivatives of those expensive PDE solvers. To the best of my knowledge, OpenMDAO is unique in this regard. This is a low level technical detail that most users never need to directly understand, but the consequence is that if you want to do any kind of high fidelity coupled optimziations (aero-structural, aero-propulsive, aero-thermal) with more than one PDE solver in the loop then OpenMDAO's architecture is going to be by far the most effective.
However, OpenMDAO does not offer a GUI. It does not have the same level of data tracking and visualization tools. Also, I know that mode-frontier offers the ability to split a single model up across multiple computers distributed across an organization. Mode Frontier, along with other tools like ModelCenter and Isight, all offer this kind of smooth user experience and code-free interaction that many find valuable.
Honestly, I'm not sure a direct comparison is really warranted. I think if you have an organization that invests in a commercial integration tool like Mode Fronteir, then you can still use OpenMDAO to create tightly coupled integrated optimizations which you can then include as boxes inside your overall integration framework.
You certainly can use OpenMDAO as a complete integration framework, and it has some advantages in that area related to derivatives and execution in distributed memory environments. But you don't have to, and it certainly does not have to be an exclusive decision.
I am currently planning to move some data sources to one place for posterior analysis.
Currently I have any data sources (databases) such as:
MSSQL
Mysql
mongodb
Postgres
Cassandra will be use for analytics in a big data pipeline. What is the best way to migrate any source to a Cassandra cluster?
I will highly recommend using NiFi for this use case. Some of benefits that I can outline right away.
Inbuilt "Processors" available for reading the data from all listed data sources and writing to Cassandra.
Very high throughput with low latency.
Rapid data acquisition pipeline development without writing a lot of code.
Ability to do "Change Data Capture" very easily later in your project, if needed.
Provides a highly concurrent model without a developer having to worry about the typical complexities of concurrency.
Is inherently asynchronous which allows for very high throughput and natural buffering even as processing and flow rates fluctuate
The resource-constrained connections make critical functions such as back-pressure and pressure release very natural and intuitive.
The points at which data enters and exits the system as well as how it flows through are well understood and easily tracked
And biggest of all, OPEN SOURCE.
You can refer Apache NiFi homepage for more information.
Hope that helps!
I've just started work at a new company who have a datawarehouse that uses some bizzare proprietary ETL built in PHP.
I'm looking for arguments as to why its worth the investment to move to a standard system such as SSIS or infomatica or something. The primary reasons I have at the moment are:
A wider and more diverse community of developers available for contract work, replacements etc.
A large online knowledge base/support networks
Ongoing updates and support will be better
What are other good high level arguments to bring a little standardisation in :)
The only real disadvantage is that a lot of the data sources are web apis returning individual row-by-row records which are more easily looped through with PHP as opposed to standard ETL.
Here are some more:
Simplifies development and deployment process.
Easy to debug and incorporate changes. Would reduce maintenance and enhancement costs.
Industry standard ETL tools perform better on large volume of data as they use various techniques like, grid computing, parallel processing, partitioning etc.
Can support many types for data as source or target. Less impact if source or target systems are migrated to a different data store.
Codes are re-usable. Same component of code can be used in multiple processes.
I have custom coded several enterprise applications for mid to large organizations to use internally (some with a minimal external footprint). I now have plans for a web project that may (hopefully) see a large userbase with more daily traffic than my previous projects have ever attained. Obviously I want my design to be scalable and maintainable. The problem is that from a physical layout perspective (servers/VMs) I do not know what to expect.
The question: What are some good resources for this? Books? Websites? I have found plenty on scalable application design, but nothing on scalable physical design.
It's hard to give exact answer without knowing something about what technologies you plan to use. The approach to the application can't be completely unaware of planned physical infrastructure if scaling is a major driver.
Caching would have to be a big concern. Also ways to expand the hardware where your data lives.
A very interesting and instructive read is the real world bio of live journal, a history of scaling, and how they grew their physical presence with a massive growth in their website. One major offshoot of their work was a new caching technology, memcached, which is now used by FaceBook among others. It is surprisingly honest.
The High Scalability blog is good. You can look at some of their examples that go over the physical parts of large sites. I would say the common first level physical scaling technique would be a load balancer. That is pretty easy but at the simplest you still have a database that is a potential bottleneck. Most of the physical parts of scaling require you to just add more and the real issues come in where you are forced to use just one of something.
I keep hearing from associates about grid computing which, from what I can gather, is highly distributed stuff along the lines of SETI#Home.
Is anyone working on these sort of systems for business use? My interest is in figuring out if there's a commercial reason for starting software development in this field.
Rendering Farms such as Pixar
Model Evaluation e.g. weather, financials, military
Architectural Engineering e.g. earthquakes.
To list a few.
Grid computing is really only needed if you have a lot of WORK that needs to be done, like folding proteins, otherwise a simple server farm will likely be plenty.
Obviously Google are major users of Grid Computing; all their search service relies on it, and many others.
Engines such as BigTable are based on using lots of nodes for storage and computation. These are commercially very useful because they're a good alternative to a small number of big servers, providing better redundancy and cost effective scaling.
The downside is that the software is fiendishly difficult to write, but Google seem to manage that one ok :)
So anything which requires big storage and/or lots of computation.
I used to work for these guys. Grid computing is used all over. Anyone who makes computer chips uses them to test designs before getting physical silicon cut. Financial websites use grids to calculate if you qualify for that loan. These days they are starting to replace big iron in a lot of places, as they tend to be cheaper to maintain over the long term.