MongoDB cookbook: Indexes
The order of fields in an index matters, you must consider Index Cardinality and Selectivity. Instead, the order of fields in a find() query doesn't affect whether it can use an index or not.
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MongoDB cookbook: Queries and Aggregations
Frequently accessed items are cached in memory, so that MongoDB can provide optimal response time.
Run a Celery task at a specific time
You are able to run any Celery task at a specific time through eta (means "Estimated Time of Arrival") parameter.
Timezone in Python: Offset-naive and Offset-aware datetimes
You should always store datetimes in UTC and convert to proper timezone on display.
Build a recommender system with Spark: Logistic Regression
在本篇文章中，我們將以 Ranking 階段常用的方法之一：Logistic Regression 邏輯迴歸為例，利用 Apache Spark 的 Logistic Regression 模型建立一個 GitHub repositories 的推薦系統，以用戶對 repo 的打星紀錄和用戶與 repo 的各項屬性做為特徵，預測出用戶會不會打星某個 repo（分類問題）。最後訓練出來的模型就可以做為我們的推薦系統的 Ranking 模組。不過因為 LR 是線性模型，所以通常需要大量的 Feature Engineering 來習得非線性關係。所以這篇文章的重點是 Spark ML 的 Pipeline 機制和特徵工程，不會在演算法的部分著墨太多。