Comparing version 0.7.0 to 0.7.1
@@ -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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+ Addedbinary-search-tree@0.2.1(transitive)
- Removedbinary-search-tree@0.1.4(transitive)
Updatedbinary-search-tree@0.2.1