distributed

问题 I'm trying non-GUI and distributed JMeter load-testing. Before I go distributed, I tested it locally. I installed JMeter on my laptop and setup a testplan targeting the system under test. I installed the plugin manager and install the concurrent thread plugin. I found my JMX file to be correct and working, so I decided to rollout the distributed setting. I setup three machines for load generation : load-1, load-2 and load-3. I installed JMeter as follow : apt update ; apt upgrade on all of

问题 The system in question is for a company with multiple locations. Unreliable internet speeds/availability at some locations have led to the path of a local server at each location off of which a location and a central server. The role of the local server is for each location to be able to run no matter if it is connected to the outside world or not, or to eliminate high latency if the the connection speed is less than optimal. The role of the central server is two-fold: Configuration, policy,

问题 I'm trying to run my first example of distributed training with TF. I've used the example that is in TF documentation https://www.tensorflow.org/deploy/distributed with one ps and one worker each on a different cluster. However, I'm always getting CreateSession still waiting for response from worker: /job:ps/replica:0/task:0 on the worker cluster as shown below! trainer.py import argparse import sys import tensorflow as tf FLAGS = None def main(_): ps_hosts = FLAGS.ps_hosts.split(",") worker

问题 I'm currently developing a back end and having my first run in with security laws etc. and it has complicated the design of my DB slightly: Specification Central server for app with DB containing limited user information (user_id, email, password (hashed and salted)) can be anywhere. Organisations making use of our service require that all other information be stored in-house, so the database for that particular organisation is in their building. The user IDs in our central database are used

问题 I'm making good progress with Dask.delayed. As a group, we've decided to put more time in working with graphs using Dask. I have a question about distribution. I'm seeing the following behaviour on our cluster. I start up e.g. 8 workers on each of 8 nodes each with 4 threads, say/ I then client.compute 8 graphs to create the simulated data for subsequent processing. I want to have the 8 data sets generated one per node. However, what seems to happen is, not unreasonably, the eight functions

问题 Our system creates OrientDB databases programmatically and uses one database for each customer (before anyone jump on dismissing this design, the reasons are security, possibility to move certain customer/data between datacenters/regions and the possibility to relocation to on-premise). This works great in OrientDB in single mode. However, when the database is setup in distributed mode (3 servers, on amazon). The behaviour is, to put it mildly, weird. I know the docs doesn't say anything

问题 It shows that it created cached files. But, when I go and look at the location the file is not present and when I am trying to read from my mapper it shows the File Not Found Exception. This is the code that I am trying to run: JobConf conf2 = new JobConf(getConf(), CorpusCalculator.class); conf2.setJobName("CorpusCalculator2"); //Distributed Caching of the file emitted by the reducer2 is done here conf2.addResource(new Path("/opt/hadoop1/conf/core-site.xml")); conf2.addResource(new Path("

问题 all I want to ask, now I'm running hadoop 2.6.0. So how can I separate this secondary namenode from the primary one? What's the configuration? Have I use one additional computer to become a secondary namenode, or it can be in a datanode? I need your suggest, thanks... 回答1: NameNode, Secondary NameNode, DataNodes are just names given to "machines" based on the job they perform. In a "ideal" distributed enviornment, they all can and should reside in separate machines. The only requirement for a

问题 I'm setting up airflow such that webserver runs on one machine and scheduler runs on another. Both share the same MySQL metastore database. Both instances come up without any errors in the logs but the scheduler is not picking up any DAG Runs that are created by manually triggering the DAGs via the Web UI. The dag_run table in MysQL shows few entries, all in running state: mysql> select * from dag_run; +----+--------------------------------+----------------------------+---------+-------------

问题 I have several large projects that I work on. Depending on the project and options, build times are from 10-100 minutes long, rendering me useless for that time. I do have a few extra computers laying around however. Is there anyway that I can configure these computers as 'compile nodes' so that I can still work while a build is going on? I've heard of software plugins for Visual Studio for doing this, but I've seen the price tags. I'm looking for something that's preferably free or under