In a previous post I discussed my findings for developing a simple word count app in Java, and running it against the text of a typical novel, such as Alice in Wonderland.
Here’s a summary of finding so far:
- 2012 MacBook Pro with a 2.4GHz i7 and SanDisk SSD it completed in 100ms
- New 2015 MacBook Pro with a 2.2GHz i7 and stock SSD, it completed in 82ms
- On an Ubuntu 16.04 server running under VMware ESXi on my HP DL380 Gen7 rack server configured with 2 x 2.4 GHz vCPUs, 6GB RAM it completed in 250ms (the slower time probably can be accounted for slower data access from the WD Black 7200rpm HDDs in my server which are likely an i/o bottleneck compared to loading the same data file from SSD)
Now let’s take it up a notch an throw some larger datasets into the mix, using the Yelp dataset. Taking the reviews.json file exported as a csv file running the same app on the same Ubuntu server in a VM on the DL380, performing the same word count against a 2.8GB file. With the same Java word count app this took 434,886ms to complete or just over 7 minutes. Ok, now we’re got something large enough to play with as a benchmark:
kev@esxi-ubuntu-mongodb1:~$ java -jar wordcount.jar ./data/yelp/reviewtext.csv
Incidentally, if you’re interested what the word counts from the Yelp reviews.json data file look like, here’s the first few counts in descending order before we get to some terms you’d expect in Yelp reviews, like ‘good’ and ‘food’:
the : 22169071 and : 18025282 I : 13845506 a : 13416437 to : 12952150 was : 9340074 of : 7811688 : 7600402 is : 6513076 for : 6207595 in : 5895711 it : 5604281 The : 4702963 that : 4446918 with : 4353165 my : 4188709 but : 3659431 on : 3642918 you : 3570824 have : 3311139 this : 3270666 had : 3015103 they : 3001066 not : 2829568 were : 2814656 are : 2660714 we : 2639049 at : 2624837 so : 2378603 place : 2358191 be : 2276537 good : 2232567 food : 2215236
Now let’s look at re-writing the analysis using Apache Spark. The equivalent code using the Spark API for loading the data set and performing the wordcount turned out to be like this (although if you search for Apache Spark word count, there’s many different ways you could use the available apis to implement a word count):
JavaRDD<String> lines = spark.read().textFile(filepath).javaRDD(); JavaPairRDD<String, Integer> words = lines .flatMap(line -> Arrays.asList(line.split("\\s+")).iterator()) .mapToPair(word -> new Tuple2<>(word, 1)) .reduceByKey((x, y) -> x + y); List<Tuple2<String, Integer>> results = words.collect();
Submitting the job to run on a standalone Spark node (an Ubuntu Server 16.04 VM on ESXi) with 1 core (–master local )
./bin/spark-submit --class "kh.textanalysis.spark.SparkWordCount" --master local ../spark-word-count-0.0.1-SNAPSHOT.jar ../data/yelp/reviewtext.csv
… the job completes in 336,326ms or approx 5.6 minutes. At this point this is with minimal understanding of how best to approach or structure an effective Spark job, but we’re already made an improvement and this first test is with a single Spark local node, and no additional worker nodes in the cluster.
Next, with a standalone local node, and 2 cores (–master local) :
170261ms, or 2.8 mins. Now we’re talking.
Let’s trying deploying the master node, and then add some worker nodes.
17/11/07 18:43:33 INFO StandaloneAppClient$ClientEndpoint: Connecting to master spark://192.168.1.82:7077…
17/11/07 18:43:33 WARN StandaloneAppClient$ClientEndpoint: Failed to connect to master 192.168.1.82:7077
My first guess is that the default 7077 port is not open, so:
sudo ufw allow 7077
Retrying, now the job has submitted, and starts up ok but gives this error.
17/11/07 18:47:28 INFO TaskSchedulerImpl: Adding task set 0.0 with 22 tasks
17/11/07 18:47:44 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
Taking a look at the master node web ui on 8080:
True, we have the master started but no workers yet, so let’s start up 1 slave node first (another Ubuntu Server 16.04 VM on ESXi):
$ ./start-slave.sh -m 1G spark://192.168.1.82:7077
starting org.apache.spark.deploy.worker.Worker, logging to /home/kev/spark-2.2.0-bin-hadoop2.7/logs/spark-kev-org.apache.spark.deploy.worker.Worker-1-ubuntuspk1.out
Now we’ve got 1 worker up and ready:
Submitting the same job again to the master node, and now there’s a FileNotFound on not finding the file we’re attempting to process:
Exception in thread “main” org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 6, 192.168.1.89, executor 0): java.io.FileNotFoundException: File file:/home/kev/data/yelp/reviewtext.csv does not exist
It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running ‘REFRESH TABLE tableName’ command in SQL or by recreating the Dataset/DataFrame involved.
I was curious how the datafile would be shared with the slave nodes and this answers my question, I guess it does not (or the way I’ve implemented my job so far assumes the data file is local). Clearly have some more work to do in this area to work out what the approach is to share the datafile between the nodes. In the meantime, I’m just going to copy the same file across each of the worker nodes to get something up and running. I’m guessing a better way would be to mount a shared drive between each of the workers, but I’ll come back to this later.
Datafile copied to my worker VMs, and restarting, now we’ve got the job running on 1 slave, 1 core, 1GB:
Here’s an interesting view of the processing steps in my job:
Taking a quick look at the HP server ILO, the CPUs are barely breaking a sweat and fan speeds are still low:
The Spark dashboard shows completion times, so now we’re at 5.3 mins:
Let’s add an extra vCPU to the first worker node (I need to reconfigure my VM in ESXi and then restart). The console’s showing an additional stopped worker from a second slave VM that I’ll start up for the last test. First, 2 vCPUs, starting up with – “-c 2” for 2 cores:
./start-slave.sh -c 2 -m 1G spark://192.168.1.82:7077
166773 ms, 2.7 minutes. Looking good!
Now with the additional second slave node, also started with 2 vCPUs, so now we have 2 worker slave nodes, 4 vCPUs total:
During the middle of the run, checking fan speeds – warming up and fans are running a little faster, but nothing crazy so far:
102977ms, or 1.7 minutes!
Curious how far we can go, lets try 2 worker slave nodes, 4vCPUs each, and bump up the available RAM to 3GB. Reconfigure my VMs, and off we go:
./start-slave.sh -c 4 -m 3G spark://192.168.1.82:7077
81998ms, or 1.4 minutes!
Still pretty good although the performance gains for doubling the cores per worker and adding more RAM seems to be leveling off and now we’re not seeing the same magnitude of improvements, so possible at this point it might be more interesting to add additional slave worker nodes. I’ve been creating my ESXi VMs by hand up until this point, so maybe this is the time to looking into some automation for spinning up multiple copies of the same VM.
Let’s summarize the results so far, running the standalone Java 8 app on the same HP server as Spark, and then comparing the various Spark configurations so far:
I’ve got ample resources left on my HP DL380 G7 rack server to run a few more VMs equally sized to what I have running, so maybe if I can work out an easy way to template the VMs I have so far, I’ll spin up some additional VMs as worker nodes and see what’s the fastest processing time I can get with the hardware I have. More updates to come later.