[ale] HADTOO: Automatic-node-generating Hadoop cluster on Gentoo Linux

dev null zero two dev.null.02 at gmail.com
Fri Sep 7 16:52:25 EDT 2018


that is pretty incredible (never thought to use Gentoo for this purpose).

have you thought about using orchestration tools for this (Kubernetes etc.)?

On Fri, Sep 7, 2018 at 4:46 PM Jeff Hubbs via Ale <ale at ale.org> wrote:

> For the past few months, I've been operating an Apache Hadoop cluster at
> Emory University's Goizueta Business School. That cluster is
> Gentoo-Linux-based and consists of a dual-homed "edge node" and three
> 16-GiB-RAM 16-thread two-disk "worker" nodes. The edge node provides NAT
> for the active cluster nodes and holds a complete mirror of the Gentoo
> package repository that is updated nightly. There is also an auxiliary edge
> node (a one-piece Dell Vostro 320) with xorg and xfce that I mostly use to
> display exported instances of xosview from all of the other nodes so that I
> can keep an eye on the cluster's operation. Each of the worker nodes
> carries a standalone Gentoo Linux instance that was flown in via rsync from
> another node while booted to a liveCD-style distribution (SystemRescueCD,
> which happens to be Gentoo-based).
>
> I have since set up the main edge node to form a "shadow cluster" in
> addition to the one I've been operating. Via iPXE and dnsmasq on the edge
> node, any x86_64 system that is connected to the internal cluster network
> and allowed to PXE-boot will download a stripped-down Gentoo instance via
> HTTP (served up by nginx), boot to this instance in RAM, and execute a bash
> script that finds, partitions, and formats all of that system's disks,
> downloads and writes to those disks a complete Gentoo Linux instance,
> installs and configures the GRUB bootloader, sets a hostname based on the
> system's first NIC's MAC address, and reboots the system into that
> freshly-written instance.
>
> At present, there is only one read/write NFS export on the edge node and
> it holds a flat file that Hadoop uses as a list of available worker nodes.
> The list is populated by the aforementioned node setup script after the
> hostname is generated.
>
> Both the PXE-booted Gentoo Linux instance and the on-disk instance are
> managed within a chroot on the edge node in a manner not unlike how Gentoo
> Linux is conventionally installed on a system. Once set up as desired,
> these instances are compressed into separate squashfs files and placed in
> the nginx doc root. In the case of the PXE-booted instance, there is an
> intermediate step where much of the instance is stripped away just to
> reduce the size of the squashfs file, which is currently 431MiB. The full
> cluster node distribution file is 1.6GiB but I sometimes exclude the kernel
> source tree and local package meta-repository to bring it down to 1.1GiB.
> The on-disk footprint of the complete worker node instance is 5.9GiB.
>
> The node setup script takes the first drive it finds and GPT-partitions it
> six ways: 1) a 2MiB "spacer" for the bootloader; 2) 256MiB for /boot; 3)
> 32GiB for root; 4) 2xRAM for swap (this is WAY overkill; it's set by ratio
> in the script and a ratio of one or less would suffice); 5) 64GiB for
> /tmp/hadoop-yarn (more about this later); 6) whatever is left for /hdfs1.
> Any remaining disks identified are single-partitioned as /hdfs2, /hdfs3,
> etc. All partitions are formatted btrfs with the exception of /boot, which
> is vfat for UEFI compatibility (a route I went down because I have one old
> laptop I found that was UEFI-only and I expect that will become more the
> case than less over time). A quasi-boolean in the script optionally enables
> compression at mount time for /tmp/hadoop-yarn.
>
> One of Gentoo Linux's strengths is the ability to compile software
> specifically for the CPU but the node instance is set up with the gcc
> option -mtune=generic. Another quasi-boolean setting in the node setup
> script will change that to -march=native but that change will only
> effectuate when packages are built or rebuilt locally (as opposed to in
> chroot on the edge node, where everything must be built generic). I can
> couple this feature with another feature to optionally rebuild all the
> system's binaries native but that's an operation that would take a fair bit
> of time (that's over 500 packages and only some of them would affect
> cluster operation). Similarly, in the interest of run-what-ya-brung
> flexibility, I'm using Gentoo's genkernel utility to generate a kernel and
> initrd befitting a liveCD-style instance that will boot on basically any
> x86-64 along with whatever NICs and disk controllers it finds.
>
> I am using the Hadoop binary distribution (currently 3.1.1) as distributed
> directly by Apache (no HortonWorks; no Cloudera). Each cluster node has its
> own Hadoop distribution and each node's Hadoop distribution has
> configuration features both in common and specific to that node, modified
> in place by the node setup script. In the latter case, the amount of
> available RAM, the number of available CPU threads, and the list of
> available HDFS partitions on a system are flown into the proper local
> config files. Hadoop services run in a Java VM; I am currently using the
> IcedTea 3.8.0 source distribution supplied within Gentoo's packaging
> system. I have also run it under the IcedTea binary distribution and the
> Oracle JVM with equal success.
>
> Hadoop has three primary constructs that make it up. HDFS (Hadoop
> Distributed File System) consists of a NameNode daemon that runs on a
> single machine and controls the filesystem namespace and user access to it;
> DataNode daemons run on each worker node and coordinate between the
> NameNode daemon and the local machine's on-disk filesystem. You access the
> filesystem with command-line-like options to the hdfs binary like -put,
> -get, -ls, -mkdir, etc. but in the on-disk filesystem underneath
> /hdfs1.../hdfsN, the files you write are cut up into "blocks" (default
> size: 128MiB) and those blocks are replicated (default: three times) among
> all the worker nodes. My initial cluster with standalone workers reported
> 7.2TiB of HDFS available spread across six physical spindles. As you can
> imagine, it's possible to accumulate tens of TiB of HDFS across only a
> handful of nodes but doing so isn't necessarily helpful.
>
> YARN (Yet Another Resource Negotiator) is the construct that manages the
> execution of work among the nodes. Part of the whole point behind Hadoop is
> to *move the processing to where the data is *and it's YARN that
> coordinates all that. It consists of a ResourceManager daemon that
> communicates with all the worker nodes and NodeManager daemons that run on
> each of the worker nodes. You can run the ResourceManager daemon and HDFS'
> NameNode daemon on the same machines that act as worker nodes but past a
> point you won't want to and past *that* point you'd want to run each of
> NameNode and ResourceManager on two separate machines. In that regime,
> you'd have two machines dedicated to those roles (their names would be
> taken out of the centrally-located workers file) and the rest would run
> both the DataNode and NodeManager daemons, forming the HDFS storage
> subsystem and the YARN execution subsystem.
>
> There is another construct, MapReduce, whose architecture I don't fully
> understand yet; it comes into play as a later phase in Hadoop computations
> and there is a JobHistoryServer daemon associated with it.
>
> Another place where the bridge is out with respect to my understanding of
> Hadoop is coding for it - but I'll get there eventually. There are other
> apps like Apache's Spark and Hive that use HDFS and/or YARN that I have
> better mental insight into, and I have successfully gotten Python/Spark
> demo programs to run on YARN in my cluster.
>
> One thing I have learned is that Hadoop clusters do not "genericize" well.
> When I first tried running the Hadoop-supplied teragen/terasort example
> (goal: make a file of 10^10 100-character lines and sort it), it failed for
> want of space available in /tmp/hadoop-yarn but it ran perfectly when the
> file was cut down to 1/100th that size. For my PXE-boot-based cluster, I
> gave my worker nodes a separate partition for /tmp/hadoop-yarn and gave it
> optional transparent compression. There are a lot of parameters for
> controlling things like minimum size and minimum size increment of memory
> containers and JVM parameters that I haven't messed with but to optimize
> the cluster for a given job, one would expect to.
>
> What I have right now - basically, a single Gentoo Linux instance for
> installation on a dual-homed edge node - is able to generate a working
> Hadoop cluster with an arbitrary number of nodes, limited primarily by
> space, cooling, and electric power (the Dell Optiplex desktops I'm using
> right now max out at about an amp, so you have to be prepared to supply at
> least N amps for N nodes). They can be purpose-built rack-mount servers, a
> lab environment full of thin clients, or wire shelf units full of discarded
> desktops and laptops.
>
> - Jeff
>
>
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-- 
Sent from my mobile. Please excuse the brevity, spelling, and punctuation.
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