> data2_tbl <- copy_to(sc, FB_tbl) #sc as spark connection
> idx <- tk_index(data2_tbl)
Warning message:
In tk_index.default(data2_tbl) :
`tk_index` is not designed to work with objects of class tbl_spark.
I have a couple of questions to the group:
Does sparklyr have support on time series like they have on the other ml_* algorithms?
We also tried and found the spark-ts package that supports time series in Spark.
I have not found good materials on how to use it. Does anyone have some documentations or experience on this?
Does sparklyr have support on time series like they have on the other ml_* algorithms?
It doesn't, because Spark doesn't. All ml_ or ft_ methods are just simple wrappers around corresponding Spark algorithms.
We also tried and found the spark-ts package that supports time series in Spark.
At this moment there is no actively developed, open source, times series analysis tool for Spark. Both spark-timeseries and flint don't seem to be maintained anymore.
This partially reflects Spark computing model, which is a poor fit for time series processing. Expressing sequential relationships in Spark is hard and usually expensive, and many time series analysis techniques, are just a bad fit for distributed processing due to their global dependencies.
Related
Just wondering, is there somewhere a list of questions to ask ourselves in order to know whether Spark is the right tool or not ?
Once again I spent part of the week implementing a POC with Apache Spark in order to compare the performance against pure python code and I was baffled when I saw the 1/100 ratio (in favor of python).
I know that Spark is a "big data" tool and everyone keeps saying "Spark is the right tool to process TB/PB of data" but I think that is not the only thing to take into account.
In brief, my question is, when given small data as input, how can I know if the computing will be consuming enough so that Spark can actually improve things ?
I'm not sure if there is such a list, but if there was, the first question would probably be
Does your data fit on a single machine?
And if the answer is 'Yes', you do not need Spark.
Spark is designed to process lots of data such that it cannot be handled by a single machine, as an alternative to Hadoop, in a fault-tolerant manner.
There are lots of overheads, such as fault-tolerance and network, associated with operating in a distributed manner that cause the apparent slow-down when compared to traditional tools on a single machine.
Because Spark can be used as a parallel processing framework on a small dataset, does not mean that it should be used in such a way. You will get faster results and less complexity by using, say, Python, and parallelizing your processing using threads.
Spark excels when you have to process a dataset that does not fit onto a single machine, when the processing is complex and time-consuming and the probability of encountering an infrastructure issue is high enough and a failure would result in starting again from scratch.
Comparing Spark to native Python is like comparing a locomotive to a bicycle. A bicycle is fast and agile, until you need to transport several tonnes of steel from one end of the country to the other: then - not so fun.
I heard about Whole-Stage Code Generation for sql to optimize queries.
through p539-neumann.pdf & sparksql-sql-codegen-is-not-giving-any-improvemnt
But unfortunately no one gave answer to above question.
Curious to know about what are the scenarios to use this feature of Spark 2.0. But didn't get proper use-case after googling.
Whenever we are using sql, can we use this feature? if so, any proper use case to see this working?
When you are using Spark 2.0, code generation is enabled by default. This allows for most DataFrame queries you are able to take advantage of the performance improvements. There are some potential exceptions such as using Python UDFs that may slow things down.
Code generation is one of the primary components of the Spark SQL engine's Catalyst Optimizer. In brief, the Catalyst Optimizer engine does the following:
(1) analyzing a logical plan to resolve references,
(2) logical plan optimization
(3) physical planning, and
(4) code generation
A great reference to all of this are the blog posts
Deep Dive into Spark SQL’s Catalyst
Optimizer
Apache Spark as a Compiler: Joining a Billion Rows per Second on a
Laptop
HTH!
I hope to use MC-Stan on Spark, but it seems there is no related page searched by Google.
I wonder if this approach is even possible on Spark, therefore I would appreciate if someone let me know.
Moreover, I also wonder what is the widely-used approach to use MCMC on Spark. I heard Scala is widely used, but I need some language that has a decent MCMC library such as MC-Stan.
Yes it's certainly possible but requires a bit more work. Stan (and popular MCMC tools that I know of) are not designed to be run in a distributed setting, via Spark or otherwise. In general, distributed MCMC is an area of active research. For a recent review, I'd recommend section 4 of Patterns of Scalable Bayesian Inference (PoFSBI). There are multiple possible ways you might want to split up a big MCMC computation but I think one of the more straightforward ways would be splitting up the data and running an off-the-shelf tool like Stan, with the same model, on each partition. Each model will produce a subposterior which can be reduce'd together to form a posterior. PoFSBI discusses several ways of combining such subposteriors.
I've put together a very rough proof of concept using pyspark and pystan (python is the common language with the most Stan and Spark support). It's a rough and limited implementation of the weighted-average consensus algorithm in PoFSBI, running on the tiny 8-schools dataset. I don't think this example would be practically very useful but it should provide some idea of what might be necessary to run Stan as a Spark program: partition data, run stan on each partition, combine the subposteriors.
Is there any sense to use Spark (in particular, MLlib) on a single node (besides the goal of learning this technology)?
Is there any improvement in speed?
Are you comparing this to using a non-Spark machine learning system?
It really depends what the capabilities are of the other library you might use.
If, for example, you've got all your training data stored in Parquet files, then Spark makes it very easy to read in those files and work with, whether that's on 1 machine or 100.
I have read a dozen pages of docs, and it seems that:
I can skip learning the scala part
the API is completely implemented in python (I don't need to learn scala for anything)
the interactive mode works as completely and as quickly as the scala shell and troubleshooting is equally easy
python modules like numpy will still be imported (no crippled python environment)
Are there fall-short areas that will make it impossible?
In recent Spark releases (1.0+), we've implemented all of the missing PySpark features listed below. A few new features are still missing, such as Python bindings for GraphX, but the other APIs have achieved near parity (including an experimental Python API for Spark Streaming).
My earlier answers are reproduced below:
Original answer as of Spark 0.9
A lot has changed in the seven months since my original answer (reproduced at the bottom of this answer):
Spark 0.7.3 fixed the "forking JVMs with large heaps" issue.
Spark 0.8.1 added support for persist(), sample(), and sort().
The upcoming Spark 0.9 release adds partial support for custom Python -> Java serializers.
Spark 0.9 also adds Python bindings for MLLib (docs).
I've implemented tools to help keep the Java API up-to-date.
As of Spark 0.9, the main missing features in PySpark are:
zip() / zipPartitions.
Support for reading and writing non-text input formats, like Hadoop SequenceFile (there's an open pull request for this).
Support for running on YARN clusters.
Cygwin support (Pyspark works fine under Windows powershell or cmd.exe, though).
Support for job cancellation.
Although we've made many performance improvements, there's still a performance gap between Spark's Scala and Python APIs. The Spark users mailing list has an open thread discussing its current performance.
If you discover any missing features in PySpark, please open a new ticket on our JIRA issue tracker.
Original answer as of Spark 0.7.2:
The Spark Python Programming Guide has a list of missing PySpark features. As of Spark 0.7.2, PySpark is currently missing support for sample(), sort(), and persistence at different StorageLevels. It's also missing a few convenience methods added to the Scala API.
The Java API was in sync with the Scala API when it was released, but a number of new RDD methods have been added since then and not all of them have been added to the Java wrapper classes. There's a discussion about how to keep the Java API up-to-date at https://groups.google.com/d/msg/spark-developers/TMGvtxYN9Mo/UeFpD17VeAIJ. In that thread, I suggested a technique for automatically finding missing features, so it's just a matter of someone taking the time to add them and submit a pull request.
Regarding performance, PySpark is going to be slower than Scala Spark. Part of the performance difference stems from a weird JVM issue when forking processes with large heaps, but there's an open pull request that should fix that. The other bottleneck comes from serialization: right now, PySpark doesn't require users to explicitly register serializers for their objects (we currently use binary cPickle plus some batching optimizations). In the past, I've looked into adding support for user-customizable serializers that would allow you to specify the types of your objects and thereby use specialized serializers that are faster; I hope to resume work on this at some point.
PySpark is implemented using a regular cPython interpreter, so libraries like numpy should work fine (this wouldn't be the case if PySpark was written in Jython).
It's pretty easy to get started with PySpark; simply downloading a pre-built Spark package and running the pyspark interpreter should be enough to test it out on your personal computer and will let you evaluate its interactive features. If you like to use IPython, you can use IPYTHON=1 ./pyspark in your shell to launch Pyspark with an IPython shell.
I'd like to add some points about why many people who have used both APIs recommend the Scala API. It's very difficult for me to do this without pointing out just general weaknesses in Python vs Scala and my own distaste of dynamically typed and interpreted languages for writing production quality code. So here are some reasons specific to the use case:
Performance will never be quite as good as Scala, not by orders, but by fractions, this is partly because python is interpreted. This gap may widen in future as Java 8 and JIT technology becomes part of the JVM and Scala.
Spark is written in Scala, so debugging Spark applications, learning how Spark works, and learning how to use Spark is much easier in Scala because you can just quite easily CTRL + B into the source code and read the lower levels of Spark to suss out what is going on. I find this particularly useful for optimizing jobs and debugging more complicated applications.
Now my final point may seem like just a Scala vs Python argument, but it's highly relevant to the specific use case - that is scale and parallel processing. Scala actually stands for Scalable Language and many interpret this to mean it was specifically designed with scaling and easy multithreading in mind. It's not just about lambda's, it's head to toe features of Scala that make it the perfect language for doing Big Data and parallel processing. I have some Data Science friends that are used to Python and don't want to learn a new language, but stick to their hammer. Python is a scripting language, it was not designed for this specific use case - it's an awesome tool, but the wrong one for this job. The result is obvious in the code - their code is often 2 - 5x longer than my Scala code as Python lacks a lot of features. Furthermore they find it harder to optimize their code as they are further away from the underlying framework.
Let me put it this way, if someone knows both Scala and Python, then they will nearly always choose to use the Scala API. The only people IME that use Python are those that simply do not want to learn Scala.