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nedb - npm Package Compare versions

Comparing version 0.7.0 to 0.7.1

4

lib/indexes.js

@@ -1,2 +0,2 @@

var BinarySearchTree = require('binary-search-tree').BinarySearchTree
var BinarySearchTree = require('binary-search-tree').AVLTree
, model = require('./model')

@@ -117,3 +117,3 @@ , _ = require('underscore')

Index.prototype.update = function (oldDoc, newDoc) {
if (util.isArray(oldDoc)) { this.updateMultipleDocs(oldDoc); }
if (util.isArray(oldDoc)) { this.updateMultipleDocs(oldDoc); return; }

@@ -120,0 +120,0 @@ this.remove(oldDoc);

{
"name": "nedb",
"version": "0.7.0",
"version": "0.7.1",
"author": {

@@ -25,3 +25,3 @@ "name": "tldr.io",

"underscore": "~1.4.4",
"binary-search-tree": "0.1.4"
"binary-search-tree": "0.2.1"
},

@@ -28,0 +28,0 @@ "devDependencies": {

@@ -295,5 +295,3 @@ # NeDB (Node embedded database)

Notes:
* The `_id` is automatically indexed with a unique constraint, so queries specifying a value for it are very fast.
* Currently, indexes are implemented as binary search trees. I will use self-balancing binary search trees in the future to guarantee a consistent performance (the index on `_id` is already balanced since the `_id` is randomly generated).
Note: the `_id` is automatically indexed with a unique constraint, no need to call `ensureIndex` on it.

@@ -336,6 +334,6 @@

containing 10,000 documents, with indexing and no pipelining:
* Insert: **6,180 ops/s**
* Find: **42,370 ops/s**
* Update: **4,730 ops/s**
* Remove: **3,750 ops/s**
* Insert: **5,950 ops/s**
* Find: **41,320 ops/s**
* Update: **4,490 ops/s**
* Remove: **3,220 ops/s**

@@ -342,0 +340,0 @@ You can run the simple benchmarks I use by executing the scripts in the `benchmarks` folder. Run them with the `--help` flag to see how they work.

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