blaze

How do you install blaze natively (i.e., not in a virtual environment) in Python? The only instructions I find are on in the package's doc (see link), and here , in a virtual environment. I didn't find any instructions anywhere online for this, but it's relatively straightforward. About my platform/tools I used: Mac OSX (Mountain Lion) Python 2.7.3 homebrew pip It looks like you might need to install Cython, not sure as I already had it installed. You can do this with pip install Cython . First, brew install llvm . Here are the packages you need. You can pip all of them: llvmpy numba meta ply

I am trying to use Blaze/Odo to read a large (~70M rows) result set from Redshift. By default SqlAlchemy witll try to read the whole result into memory, before starting to process it. This can be prevented by either execution_options(stream_results=True) on the engine/session or yield_per(sane_number) on the query. When working from Blaze SqlAchemy queries are generated behind the covers, leaving the execution_options approach. Unfortunately the following throws and error. from sqlalchemy import create_engine from blaze import Data redshift_params = (redshift_user, redshift_pass, redshift

1.首先安装的是开发版，按照提示一步一步安装完成后，执行C:\Blaze\Advisor75\bin目录下的verifyInstall.bat文件，进行编译。 2.在C:\Blaze\Advisor75\doc\translatedPDF目录下有Chinese_GettingStartedAndTutorial.pdf文件，安装此文件教程配置一个示例。（注意：在使用eclipse时，需要导入Blaze插件包，点击eclipse>Help>Install new softWare>add>local,在local选择C:\Blaze\Advisor75目录下的 EclipsePluginUpdateSite文件夹即可。） 3.启动blaze服务，在C:\Blaze\Advisor75\examples\bin目录下，先执行bulid文件构建，在执行startServers.bat，启动过程中会报错，我这里去网上下载了报错的jar包，放在了C:\Blaze\Advisor75\examples\lib目录下，csrfguard-3.0.0.jar，之后启动正常。 4.部署版由于知识单纯的部署，没有启动文件，暂时没有启动成功。 。 原文：http://blog.51cto.com/13809405/2144006

How to read a modestly sized Parquet data-set into an in-memory Pandas DataFrame without setting up a cluster computing infrastructure such as Hadoop or Spark? This is only a moderate amount of data that I would like to read in-memory with a simple Python script on a laptop. The data does not reside on HDFS. It is either on the local file system or possibly in S3. I do not want to spin up and configure other services like Hadoop, Hive or Spark. I thought Blaze/Odo would have made this possible: the Odo documentation mentions Parquet, but the examples seem all to be going through an external