benchmark | Large scale query engine benchmark | Performance Testing library
kandi X-RAY | benchmark Summary
kandi X-RAY | benchmark Summary
Large scale query engine benchmark
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Top functions reviewed by kandi - BETA
- Launch security groups .
- Convert a query string to a query string .
- Run the benchmark .
- Run the Hive benchmark .
- Prepare the watershed dataset
- Runs the Hive command .
- Parse command line arguments .
- Prepare the Hive dataset
- Start the hcat server .
- Run Impala benchmark .
benchmark Key Features
benchmark Examples and Code Snippets
Lines of Code : 82License : Permissive (MIT License)
Copydef benchmark() -> None:
"""
Benchmark code for comparing 3 functions,
with 3 different length int values.
"""
print("\nFor small_num = ", small_num, ":")
print(
"> sum_of_digits()",
"\t\tans =",
def benchmark() -> None:
"""
Benchmark code for comparing 3 functions,
with 3 different length int values.
"""
print("\nFor small_num = ", small_num, ":")
print(
"> num_digits()",
"\t\tans =",
num def _benchmark_series(self, label, series, benchmark_id):
"""Runs benchmark the given series."""
# Decides a proper number of iterations according to the inputs.
def compute_num_iters(map_num_calls, inter_op, element_size, batch_size):
Community Discussions
Trending Discussions on benchmark
QUESTION
I am new to rust and I was reading up on using futures and async / await in rust, and built a simple tcp server using it. I then decided to write a quick benchmark, by sending requests to the server at a constant rate, but I am having some strange issues.
The below code should send a request every 0.001 seconds, and it does, except the program reports strange run times. This is the output:
...ANSWER
Answered 2021-Jun-15 at 20:06You are not measuring the elapsed time correctly:
total_send_timemeasures the duration of thespawn()call, but as the actual task is executed asynchronously,start_in.elapsed()does not give you any information about how much time the task actually takes.The
ran intime, as measured bystart.elapsed()is also not useful at all. As you are using blocking sleep operation, you are just measuring how much time your app has spent in thestd::thread::sleep()Last but not least, your
time_to_sleepcalculation is completely incorrect, because of the issue mentioned in point 1.
QUESTION
When a divide-and-conquer recursive function doesn't yield runtimes low enough, which other improvements could be done?
Let's say, for example, this power function taken from here:
ANSWER
Answered 2021-Jun-15 at 17:36The primary optimization you should use here is common subexpression elimination. Consider your first piece of code:
QUESTION
So I was really ripping my hair out why two different sessions of R with the same data were producing wildly different times to complete the same task.
After a lot of restarting R, cleaning out all my variables, and really running a clean R, I found the issue: the new data structure provided by vroom and readr is, for some reason, super sluggish on my script. Of course the easiest thing to solve this is to convert your data into a tibble as soon as you load it in. Or is there some other explanation, like poor coding praxis in my functions that can explain the sluggish behavior? Or, is this a bug with recent updates of these packages? If so and if someone is more experienced with reporting bugs to tidyverse, then here is a repex showing the behavior cause I feel that this is out of my ballpark.
ANSWER
Answered 2021-Jun-15 at 14:37This is the issue I had in mind. These problems have been known to happen with vroom, rather than with the spec_tbl_df class, which does not really do much.
vroom does all sorts of things to try and speed reading up; AFAIK mostly by lazy reading. That's how you get all those different components when comparing the two datasets.
With vroom:
QUESTION
I'm learning that Golang channels are actually slower than many alternatives provided by the language. Of course, they are really easy to grasp but because they are a high level structure, they come with some overhead.
Reading some articles about it, I found someone benchmarking the channels here. He basically says that the channels can transfer 10 MB/s, which of course must be dependant on his hardware. He then says something that I haven't completely understood:
If you just want to move data quickly using channels then moving it 1 byte at a time is not sensible. What you really do with a channel is move ownership of the data, in which case the data rate can be effectively infinite, depending on the size of data block you transfer.
I've seen this "move ownership of data" in several places but I haven't seen a solid example illustrating how to do it instead of moving the data itself.
I wanted to see an example in order to understand this best practice.
...ANSWER
Answered 2021-Jun-14 at 03:22Moving data over a channel:
QUESTION
Below is the code and the error that I'm getting while testing in Lambda. I'm a newbie in python & serverless. Please help. This is created for uploading the findings from the security hub to S3 for POC.
...ANSWER
Answered 2021-Jun-12 at 16:33When we use Lambda we need to write our code inside the lambda_handler method
"def lambda_handler(event, context):" .
As you mentioned you are using lambda to run this code then probably the below code should work for you.
QUESTION
I am running a TPC-DS benchmark for Spark 3.0.1 in local mode and using sparkMeasure to get workload statistics. I have 16 total cores and SparkContext is available as
Spark context available as 'sc' (master = local[*], app id = local-1623251009819)
Q1. For local[*], driver and executors are created in a single JVM with 16 threads. Considering Spark's configuration which of the following will be true?
- 1 worker instance, 1 executor having 16 cores/threads
- 1 worker instance, 16 executors each having 1 core
For a particular query, sparkMeasure reports shuffle data as follows
shuffleRecordsRead => 183364403
shuffleTotalBlocksFetched => 52582
shuffleTotalBlocksFetched => 52582
shuffleLocalBlocksFetched => 52582
shuffleRemoteBlocksFetched => 0
shuffleTotalBytesRead => 1570948723 (1498.0 MB)
shuffleLocalBytesRead => 1570948723 (1498.0 MB)
shuffleRemoteBytesRead => 0 (0 Bytes)
shuffleRemoteBytesReadToDisk => 0 (0 Bytes)
shuffleBytesWritten => 1570948723 (1498.0 MB)
shuffleRecordsWritten => 183364480
Q2. Regardless of the query specifics, why is there data shuffling when everything is inside a single JVM?
...ANSWER
Answered 2021-Jun-11 at 05:56- executor is a jvm process when you use
local[*]you run Spark locally with as many worker threads as logical cores on your machine so : 1 executor and as many worker threads as logical cores. when you configureSPARK_WORKER_INSTANCES=5inspark-env.shand execute these commandsstart-master.shandstart-slave.sh spark://local:7077to bring up a standalone spark cluster in your local machine you have one master and 5 workers, if you want to send your application to this cluster you must configure application likeSparkSession.builder().appName("app").master("spark://localhost:7077")in this case you can't specify[*]or[2]for example. but when you specify master to belocal[*]a jvm process is created and master and all workers will be in that jvm process and after your application finished that jvm instance will be destroyed.local[*]andspark://localhost:7077are two separate things. - workers do their job using tasks and each task actually is a thread
i.e.
task = thread. workers have memory and they assign a memory partition to each task in order to they do their job such as reading a part of a dataset into its own memory partition or do a transformation on read data. when a task such as join needs other partitions, shuffle occurs regardless weather the job is ran in cluster or local. if you were in cluster there is a possibility that two tasks were in different machines so Network transmission will be added to other stuffs such as writing the result and then reading by another task. in local if task B needs the data in the partition of the task A, task A should write it down and then task B will read it to do its job
QUESTION
In a heatmap, how could I create a three-color gradient, with blue for negative values, red for positive values and white for zero, such that with many zero values, much of the heatmap would be white (and not light red as with the default gradient).
...ANSWER
Answered 2021-Jun-10 at 22:07You can compute the maximum absolute value in your array, then use it to set the clims argument. c.f. http://docs.juliaplots.org/latest/generated/attributes_subplot/
QUESTION
I have a large DataFrame of distances that I want to classify.
...ANSWER
Answered 2021-Jun-08 at 20:36You can vectorize the calculation using numpy:
QUESTION
I have two monotonic increasing vectors, v1 and v2 of unequal lengths. For each value in v1 (e.g., v1[1], v1[2], ...), I want to find the value in v2 that is just less than v1[i] and compute the difference.
My current code (see below) works correctly, but does not seem to scale up well. So I am looking for recommendations to improve my approach with the requirement of staying in R, or using a package I can call from R.
Example code:
...ANSWER
Answered 2021-Jun-09 at 12:59Use findInterval:
QUESTION
I am in search of performance benchmarks for querying parquet ADLS files with the standard dedicated sql pool using external tables with polybase vs. serverless sql pool and OPENROWSET views. From my base queries on a 1.5 billion record table, it does appears OPENROWSET in serverless sql pool is around 30% more performant given time for the same query, but what are the architecture that power that? Are there any readily available performance benchmarks?
...ANSWER
Answered 2021-Jun-09 at 09:33The architecture behind Azure Synapse SQL Serverless Pools and how it achieves such a strong performance is described in this paper, it is called "Polaris".
http://www.vldb.org/pvldb/vol13/p3204-saborit.pdf
Performance benchmarks have been published on multiple blogs. Be aware that this can only be a snapshot in time as those features are being improved constantly.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install benchmark
You can use benchmark like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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