We want to maintain business glossary in the organization. Multiple Data Catalogs support Business Glossary but no standard format is used. W3C standard exists to maintain this information.
How to import business glossary maintained in W3C standard to DataHub Catalog?
| package com.netflix.fenzo; | |
| import com.netflix.fenzo.plugins.SpreadingFitnessCalculators; | |
| import org.apache.mesos.Protos; | |
| import org.slf4j.Logger; | |
| import org.slf4j.LoggerFactory; | |
| import java.util.*; | |
| import java.util.stream.Collectors; |
The glibc memory allocator, at least in some versions of Linux, has an optimization to improve speed by avoiding contention when a process has a large number of concurrent threads. The supposed speedup is achieved by maintaining per-core memory pools. Essentially, with this optimization, the OS grabs memory for a given process in pretty big same-size (64MB) chunks called arenas, which are clearly visible when process memory is analyzed with pmap. Each arena is available only to its respective CPU core, so no more than one thread at a time can operate on it. Then, individual malloc() calls reserve memory within these arenas. Up to a certain maximum number of arenas (8 by default) can be allocated per each CPU core. Looks like this is maxed out when the number of threads is high and/or threads are created and destroyed frequently. The actual amount of memory utilized by the application within these arenas can be quite small. However, if an application has a large number of threads, and the ma
Probably the most expensive challenge, even when using a library like Finagle, is that an organisation will still need to invest time from its engineering team in building the glue that links the libraries with the rest of their ecosystem. Based on my experiences at SoundCloud and DigitalOcean I would estimate that following this strategy in a 100-250 engineers organisation, one would need to dedicate 1/10 of the staff to building tooling. Sometimes this cost is explicit as engineers are assigned to teams dedicated to building tooling, but more often the price tag is invisible as it manifests itself as time taken away from working on your products.
A second issue is that the setup above limits the tools, runtimes, and languages you can use for your microservices. Libraries for microservices are often written for a specific platform, be it a programming language or a runtime like the JVM. If an organisation uses platforms other tha
Client Broker compatibility - https://cwiki.apache.org/confluence/display/KAFKA/KIP-97%3A+Improved+Kafka+Client+RPC+Compatibility+Policy
Include only those checks which you think, if fails, will get cured with a pod restart
Health-check endpoints are called frequently and within fixed intervals, therefore your latencies should ideally be small. Enforce an overall upper bound for your health-check response and make sure to cancel any pending checks to the downstream dependencies in case of timeout.
If you are performing multiple dependency checks over the network, try to process them concurrently, if possible, to reduce the overall response time.
Node replacement scripts - https://github.com/ApplauseOSS/dcos-node-replacement
Add logback.xml on the classpath.
<?xml version="1.0" encoding="UTF-8"?>
<configuration>
<include resource="org/springframework/boot/logging/logback/defaults.xml"/>
<property name="LOG_FILE" value="${LOG_FILE:-${LOG_PATH:-${LOG_TEMP:-${java.io.tmpdir:-/tmp}}}/spring.log}"/>
sudo apt-get install python3
sudo apt-get install python3-pip
pip3 install virtualenv
vagrant plugin install vagrant-hostmanager
cd ~/dev/tools/dcos/dcos-vagrant-1.2.0
cp VagrantConfig.yaml.example VagrantConfig.yaml
Reduce the memory of agent node to 1GB i.s.o. 6GB
Update etc/config-1.9.yml add oauth_enabled: false