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import numpy | |
import perfplot | |
perfplot.show( | |
setup=lambda n: numpy.random.randint(0, 1000, n), | |
kernels=[ | |
lambda a: a[::-1], | |
lambda a: numpy.ascontiguousarray(a[::-1]), | |
lambda a: numpy.fliplr([a])[0] |
Below are a set of best practices that I recommend for most customers. This information is based on my experience helping hundreds of Azure Redis customers investigate various issues.
- Use Standard or Premium Tier for Production systems. The Basic Tier is a single node system with no data replication and no SLA. Also, use at least a C1 cache. C0 caches are really meant for simple dev/test scenarios since they have a shared CPU core, very little memory, are prone to "noisy neighbor", etc.
- Remember that Redis is an In-Memory data store. Read this article so that you are aware of scenarios where data loss can occur.
- Configure your client library to use a "connect timeout" of at least 10 to 15 seconds, giving the system time to connect even under higher CPU conditions. If your client or server tend to be under high load
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trait Car { | |
val doorsNb: Int | |
override def equals(a: Any) = { | |
a match { | |
case c: Car => doorsNb == c.doorsNb | |
case other => false | |
} | |
} | |
} |
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#!/bin/sh | |
# NOTE: | |
# Make sure that the value of Name, Type, TTL are the same with your DNS Record Set | |
HOSTED_ZONE_ID=<YOUR_HOSTED_ZONE_ID> | |
RESOURCE_VALUE=<YOUR_DNS_RESOURCE_VALUE-ex:IP or dns> | |
DNS_NAME=<YOUR_DNS_NAME-ex: subdomain.domain.com> | |
RECORD_TYPE=<DNS_RECORD_TYPE-ex: A, CNAME> | |
TTL=<TTL_VALUE> |
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