lmdb

What would be the performance difference (reads/writes) between some C++ implementation of in-memory B-Tree (for example google btree) and the LMDB (without taking into consideration all the feacures of LMDB such as transactions, isolation, shared access etc.)? arielf This 2014 lmdb design presentation by its architect Howard Chu covers the design and tradeoffs of lmdb . To summarize: lmdb is a copy-on-write, bottom-up updated, double-buffered, b-tree where the implementation always favors simplicity whenever it clashes with other considerations. The smart design choices make it one of the

I open a lmdb database using this code: lmdb_env = lmdb.open(source_path, readonly=True) How can I count the number of records in this database? GieBur I think it should be like this: lmdb_env = lmdb.open(lmdb_file_name, readonly=True) print lmdb_env.stat() Then it prints the directory that Jaco pasted here. You can use event.stat() . It will return the following dictionary with entries detailing the number of records in this database: {'branch_pages': 1040L, 'depth': 4L, 'entries': 3761848L, 'leaf_pages': 73658L, 'overflow_pages': 0L, 'psize': 4096L} trminh89 I found a simple solution using

LMDB is a key-value store. What types of keys and values can be stored here? Examples shows either int or char arrays .. Also I would like to know if it is possible to store related data in lmdb like we store all data related to a student in a table in RDBMS It sounds like you're not a C programmer, but in the context of the C language, the data for keys and values in LMDB are both (void *) - that is, generic pointers to anything. That means any data type that can be expressed in the programming language can be used. In other languages, through whatever bindings are provided, your options may

I am new in caffe framework and I would like to use caffe to implement the training with multi-label. I use two LMDB to save data and labels, respectively. The data LMDB is of dimension Nx1xHxW while the label LMDB is of dimension Nx1x1x3. Labels are float data. The text file is as follow: 5911 3 train/train_data/4224.bmp 13 0 12 train/train_data/3625.bmp 11 3 7 ... ... I use C++ to create LMDB. My main.cpp: #include <algorithm> #include <fstream> // NOLINT(readability/streams) #include <string> #include <utility> #include <vector> #include <QImage> #include "boost/scoped_ptr.hpp" #include

1.概述 1.1前言 　　最近在存储字模图像集的时候，需要学习LMDB，趁此机会复习了SQLite和MySQL的使用，一起整理在此。 1.2环境 　　使用win7，Python 3.5.2。 2.SQLite 2.1准备 　　SQLite是一种嵌入式数据库，它的数据库就是一个文件。Python 2.5x以上版本内置了SQLite3，使用时直接import sqlite3即可。 2.2操作流程 　　概括地讲，操作SQLite的流程是： 　　　　·通过sqlite3.open()创建与数据库文件的连接对象connection 　　　　·通过connection.cursor()创建光标对象cursor 　　　　·通过cursor.execute()执行SQL语句 　　　　·通过connection.commit()提交当前的事务，或者通过cursor.fetchall()获得查询结果 　　　　·通过connection.close()关闭与数据库文件的连接 　　详细的sqlite3模块API可以看这里： SQLite - Python 　　总结起来就是用cursor.execute()执行SQL语句，改变数据（插入、删除、修改）时用connection.commit()提交变更，查询数据时用cursor.fetchall()得到查询结果。 2.3操作实例 2.3.1建立数据库与建立表

I am trying to build a deep learning model for Saliency analysis using caffe (I am using the python wrapper). But I am unable to understand how to generate the lmdb data structure for this purpose. I have gone through the Imagenet and mnist examples and I understand that I should generate labels in the format my_test_dir/picture-foo.jpg 0 But in my case, I will be labeling each pixel with 0 or 1 indicating whether that pixel is salient or not. That won't be a single label for an image. How to generate lmdb files for a per pixel based labeling ? You can approach this problem in two ways: 1.

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I open a lmdb database using this code: lmdb_env = lmdb.open(source_path, readonly=True) How can I count the number of records in this database? 回答1: I think it should be like this: lmdb_env = lmdb.open(lmdb_file_name, readonly=True) print lmdb_env.stat() Then it prints the directory that Jaco pasted here. 回答2: I found a simple solution using for loop. Here it is: count = 0 for key, value in lmdb_env.cursor(): count = count + 1 However, I think there should be a better way using pre-defined function. 回答3: You can use event.stat() . It will

I'm trying to understand how data is interpreted in Caffe. For that I've taken a look at the Minst Tutorial Looking at the input data definition: layers { name: "mnist" type: DATA data_param { source: "mnist_train_lmdb" backend: LMDB batch_size: 64 scale: 0.00390625 } top: "data" top: "label" } I've now looked at the mnist_train_lmdb and taken one of the entries (shown in hex): 0801101C181C229006 00000000000000000000000000000000000000000000000000000000 00000000000000000000000000000000000000000000000000000000 00000000000000000000000000000000000000000000000000000000