Loading data bigger than the memory size in h2o

Question

I am experimenting with loading data bigger than the memory size in h2o.

H2o blog mentions: A note on Bigger Data and GC: We do a user-mode swap-to-disk when the Java heap gets too full, ie, you're using more Big Data than physical DRAM. We won't die with a GC death-spiral, but we will degrade to out-of-core speeds. We'll go as fast as the disk will allow. I've personally tested loading a 12Gb dataset into a 2Gb (32bit) JVM; it took about 5 minutes to load the data, and another 5 minutes to run a Logistic Regression. A note on Bigger Data and GC: We do a user-mode swap-to-disk when the Java heap gets too full, ie, you're using more Big Data than physical DRAM. We won't die with a GC death-spiral, but we will degrade to out-of-core speeds. We'll go as fast as the disk will allow. I've personally tested loading a 12Gb dataset into a 2Gb (32bit) JVM; it took about 5 minutes to load the data, and another 5 minutes to run a Logistic Regression.

Here is the R code to connect to h2o 3.6.0.8 :

h2o.init(max_mem_size = '60m') # alloting 60mb for h2o, R is running on 8GB RAM machine

gives

java version "1.8.0_65"
Java(TM) SE Runtime Environment (build 1.8.0_65-b17)
Java HotSpot(TM) 64-Bit Server VM (build 25.65-b01, mixed mode)

.Successfully connected to http://127.0.0.1:54321/ 

R is connected to the H2O cluster: 
    H2O cluster uptime:         2 seconds 561 milliseconds 
    H2O cluster version:        3.6.0.8 
    H2O cluster name:           H2O_started_from_R_RILITS-HWLTP_tkn816 
    H2O cluster total nodes:    1 
    H2O cluster total memory:   0.06 GB 
    H2O cluster total cores:    4 
    H2O cluster allowed cores:  2 
    H2O cluster healthy:        TRUE 

Note:  As started, H2O is limited to the CRAN default of 2 CPUs.
       Shut down and restart H2O as shown below to use all your CPUs.
           > h2o.shutdown()
           > h2o.init(nthreads = -1)

IP Address: 127.0.0.1 
Port      : 54321 
Session ID: _sid_b2e0af0f0c62cd64a8fcdee65b244d75 
Key Count : 3

I tried to load a 169 MB csv into h2o.

dat.hex <- h2o.importFile('dat.csv')

which threw an error,

Error in .h2o.__checkConnectionHealth() : 
  H2O connection has been severed. Cannot connect to instance at http://127.0.0.1:54321/
Failed to connect to 127.0.0.1 port 54321: Connection refused

which is indicative of out of memory error .

Question: If H2o promises loading a data set larger than its memory capacity(swap to disk mechanism as the blog quote above says), is this the correct way to load the data?

Answer 1

Swap-to-disk was disabled by default awhile ago, because performance was so bad. The bleeding-edge (not latest stable) has a flag to enable it: "--cleaner" (for "memory cleaner").
Note that your cluster has an EXTREMELY tiny memory: H2O cluster total memory: 0.06 GB That's 60MB! Barely enough to start a JVM with, much less run H2O. I would be surprised if H2O could come up properly there at all, never mind the swap-to-disk. Swapping is limited to swapping the data alone. If you're trying to do a swap-test, up your JVM to 1 or 2 Gigs ram, and then load datasets that sum more than that.

Cliff