2019年5月28日
今天記錄一下“如何優(yōu)化Tableau工作簿”的學(xué)習(xí)。
原文鏈接：https://www.dataplusscience.com/OptimizeTableau.html
Viz反應(yīng)時(shí)間長的原因：
1.文件中有一個(gè)未使用的額外數(shù)據(jù)源。
2.數(shù)據(jù)量有180萬行，但大部分都未在可視化中用到，冗余數(shù)據(jù)太多，需要將數(shù)據(jù)減少到真正需要的量。
3.過程中進(jìn)行了大量的計(jì)算。
關(guān)于性能優(yōu)化的詳細(xì)解決辦法：
https://www.tableau.com/learn/whitepapers/designing-efficient-workbooks 白皮書網(wǎng)盤下載：https://pan.baidu.com/s/1VzNhNfNkDTe4apyQDse6DQ 提取碼：dd1u
Summary：1.There is no silver bullet for inefficient workbooks. Start by looking at the performance recorder to understand where the time is going. Long-running queries? Lots of queries? Slow calculations? Complex rendering? Use this insight to focus your efforts in the right direction.
2. The recommendations in this document are just that – recommendations. While they represent a level of best practice, you need to test if they will improve performance in your specific case. Many of them can be dependent on structure of your data, and the data source you are using (e.g. flat file vs. RDBMS vs. data extract).
3. Extracts are a quick and easy way to make most workbooks run faster.
4.The cleaner your data is and the better it matches the structure of your questions (i.e. the less preparation and manipulation required), the faster your workbooks will run.
5.The majority of slow dashboards are caused by poor design – in particular, too many charts on a single dashboard, or trying to show too much data at once. Keep it simple. Allow your users to incrementally drill down to details, rather than trying to show everything then filter.
6.Work with the data you need and no more – both in terms of the fields you reference as well as the granularity of the records you return. It allows Tableau to generate fewer, better,faster queries and reduces the amount of data that needs to be moved from the data source to Tableau’s engine. It also reduces the size of your workbooks so they are easier to share and open faster.
7. While reducing the data, make sure you use filters efficiently.
8.Strings and dates are slow, numbers and Booleans are fast.
Finally, some of the recommendations in this document only have a material impact if you are working with big and/or complex data sets. What is big or complex? That depends… but it doesn’t hurt to follow these recommendations in all your workbooks as you never know when your data will grow. Practice makes perfect.
學(xué)習(xí)進(jìn)度：SQL (60%)
Python (16%)
Tableau（50%）
