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Abstract

It has already been known that Google Apps Script is a strong tool for managing Google Spreadsheets. When the values are retrieved and/or put for Google Spreadsheet, there is a case that the discontinuous cells are required to be used. This report suggests the Best Practices for processing the discontinuous cells on Google Spreadsheet. From the results of process costs, it could understand the usefulness of using the discontinuous cells with low cost using Sheets API and Class RangeList of Spreadsheet service with Google Apps Script.

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When a value is searched from the 1-dimensional array and a 2-dimensional array, after V8 runtime could be used, I use JSON object, Set object, and Map Object. But, I had never measured the process cost of this situation. In this post, I would like to introduce the process cost for searching a value using a JSON object, Set object, and Map object converted from the 1-dimensional array and 2-dimensional array.

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Kanshi Tanaike

Introduction

There is a maximum executing time for Google Apps Script (GAS). That is 6 minutes. And, in the case of the custom function and the simple trigger, it is 30 seconds. Ref So users always have to pay attention to reducing the process cost of the scripts. Especially, it is very important to know the process costs for the array processing, because array processing is often used for spreadsheets and Google APIs. I have already reported about the array processing at “Benchmark: Loop for Array Processing using Google Apps Script with V8” and “Search for Array Processing using Google Apps Script”. In this report, the process cost checking a value in a one-dimensional array using Google Apps Script has been investigated.

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This is a sample script for high-efficiency finding and replacing many values in Google Spreadsheet with the low process cost using Google Apps Script.

When the various values are replaced in Google Spreadsheet using Google Apps Script, I’m worried about the process cost. So, in this report, I would like to introduce a sample script for high-efficiency achieving this.

As the result, using a sample situation, when the process cost of the sample script using Sheets API is compared with that of the sample script using Spreadsheet services (SpreadsheetApp), it was found that the above script using Sheets API could reduce the process cost by about 70 % from the script using Spreadsheet service.

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Introduction

In order to retrieve the values from XML data, when XML data is parsed using Google Apps Script, there are several methods for parsing the data. Class XmlService, which is a built-in Class for managing XML data, might be the first way to come up with it. At Stackoverflow, it is posted questions that XML data is often parsed using Class XmlService. It is considered that Class XmlService is suitable for managing XML data.

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Introduction

When we use HTML in the Google Apps Script project, in order to show the values from the Google Apps Script side, the HTML template is used. When I used the HTML template with a large value, I understood that the process cost can be reduced by devising a script. In this report, I would like to introduce the process cost of the HTML template using the benchmark.

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• Published: September 15, 2021

• Updated: September 17, 2021

  • From the discussions, added data by changing the wait time of LockService for Web Apps.

Kanshi Tanaike

Introduction

When the users try to write to a Spreadsheet using a form, the developers have to consider the concurrent submission from the form. For example, when multiple users submit the data with the form simultaneously, all data are possibly not to be saved to the Spreadsheet. So it is considered that it is important to know the information about the concurrent writing to Google Spreadsheet using a form. In this report, such a situation was investigated.

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Introduction

Here, I would like to report the process costs for retrieving the 1st empty cell or 1st non empty cell of the specific column of Google Spreadsheet using Google Apps Script (GAS). For this situations, the following 2 patterns can be considered.

1. Retrieving 1st empty cell of specific column by searching from TOP of sheet

2. Retrieving 1st NON empty cell of specific column by searching from BOTTOM of sheet

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Description

When Google Spreadsheet is used, there is the case that the built-in functions and the custom functions in the cells are used. For the functions of Google Apps Script, there is the method for measuring the process cost. Ref But for the built-in functions, it is required to create the script for it. In this report, the script for measuring a function put in a cell has been proposed, and the process cost of the built-in functions has been measured. The proposed script can measure the process cost for the built-in functions and custom functions on Google Spreadsheet. The script is created with using Google Apps Script. When the process cost can be known for the built-in functions and custom functions, it is considered that it will be useful for the developers using Google Spreadsheet.

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Introduction

Here, I would like to report the process costs for retrieving the values from the arrays for Spreadsheet using Google Apps Script (GAS). When Spreadsheet is used with Google Apps Script, we have the following situations.

1. Retrieve values from the multiple rows in a column.

2. Retrieve values from the multiple columns in a row.

When the values are retrieved from above situations, it is required to retrieve the values from 1 dimensional array in the 2 dimensional array. In this report, the process costs for retrieving the values from the 2 dimensional array of above situations have been measured.

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Introduction

Here, I would like to report the process costs for searching values in Spreadsheet using Google Apps Script (GAS). When the values are searched in Google Spreadsheet, the following 3 patterns can be considered. Ref

1. Retrieve all values using getValues, and the values are searched from the retrieved array.
2. Use TextFinder.
3. Use Query language.

In these cases, it has already been found that the lowest process cost is to use the Query language. And about finding values from an array, I have already been reported as “Benchmark: Search for Array Processing using Google Apps Script”. But I had never summarized the process costs for TextFinder and find values from an array. So in this report, I would like to introduce this. As the result, the importance of TextFinder for retrieving the row numbers and the row values by searching a value could be understand.

Gists

• March 22, 2020
  • Published.

Kanshi Tanaike

Introduction

V8 engine got to be able to be used at Google Apps Script. By this, I have reported about the process costs with and without using V8. Ref It is considered that knowing the process costs for various methods will be useful for creating the applications with Google Apps Script. Here, I would like to introduce the process costs of each situations under V8. The situations which measured the cost are as follows.

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Introduction

Please be careful! This result can be only used for Google Apps Script.

There are a limit executing time for Google Apps Script (GAS). That is 6 minutes.¹ So users always have to pay attention to reducing the process cost of the scripts. Especially, it is very important to know the process cost for the array processing, because the array processing is often used for spreadsheet and Google APIs. I have already reported “Benchmark: Loop for Array Processing using Google Apps Script”.² At February 7, 2020, the V8 runtime got to be able to be used in my account. By this, it is considered that it is possibly changed the result of benchmark without V8 ². So I measured about this. In this report, the process cost of “loop” for the array processing using GAS has been investigated with V8 runtime.

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Introduction

Please be careful! This result can be only used for Google Apps Script.

There are a limit executing time for Google Apps Script (GAS). That is 6 minutes for Consumer and Google Apps free edition, and 30 minutes for G Suite and Early Access. ¹ So many users always have to pay attention to reducing the process cost of scripts. So it is very important to know the process cost of various situations. I have already reported the costs for various processes as the reports. ² In this report, the process cost for importing CSV data to Spreadsheet using GAS has been investigated.

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Benchmark: Reading and Writing Spreadsheet using Google Apps Script

Introduction

Please be careful! This result can be only used for Google Apps Script.

There are a limit executing time for Google Apps Script (GAS). That is 6 minutes for Consumer and Google Apps free edition, and 30 minutes for G Suite and Early Access. ¹ So many users always have to pay attention to reducing the process cost of scripts. So it is very important to know the process cost of various situations. I have already reported the costs for various processes as the reports. ² In this report, the process cost for reading and writing values for Spreadsheet using GAS has been investigated.

Gists

Benchmark: Decreasing Loop for Array Processing using Google Apps Script

Introduction

Please be careful! This result can be only used for Google Apps Script.

There are a limit executing time for Google Apps Script (GAS). That is 6 minutes. ¹ So users always have to pay attention to reducing the process cost of the scripts. Especially, it is very important to know the process cost for the array processing, because the array processing is often used for spreadsheet and Google APIs. I have already reported the process costs for various processes as reports. ^2-7 In this report, the process cost of “Decreasing loop” for the array processing using GAS has been investigated.

I updated the report of “Benchmark: Loop for Array Processing using Google Apps Script”. In this update, “reduce” was added. If this report is useful for your situation, I’m glad.

You can check this at https://gist.github.com/tanaikech/848aeafaac1ec676900bb78e3ce220b6.

Gists

Benchmark: Conditional Branch using Google Apps Script

Introduction

Please be careful! This result can be only used for Google Apps Script.

There are a limit executing time for Google Apps Script (GAS). That is 6 minutes.¹ So users always have to pay attention to reducing the process cost of the scripts. Especially, it is very important to know the process cost for the array processing, because the array processing is often used for spreadsheet and Google APIs. Recently, I have already published some reports about the process cost using GAS.^2-6 From these reports, it has found that GAS shows much different process cost from other languages. So it is important to investigate the process cost for various scenes. In this report, the process cost of “conditional branch” using GAS has been investigated.

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Benchmark: Search for Array Processing using Google Apps Script

Introduction

Please be careful! This result can be only used for Google Apps Script.

There are a limit executing time for Google Apps Script (GAS). That is 6 minutes.¹ So users always have to pay attention to reducing the process cost of the scripts. Especially, it is very important to know the process cost for the array processing, because the array processing is often used for spreadsheet and Google APIs. Recently, I have reported about the process cost of the loop for the array processing.² Also I have reported “Improved Algorithms for Summation of Array Elements” as a method for reducing the process cost.³ From these reports, it has found that GAS shows much different process cost from other languages. So it is important to investigate the process cost for various scenes. In this report, the process cost of “searching strings in an array” for the array processing using GAS has been investigated.

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By Google’s update at January 19, 2018, fetchAll method was added to the UrlFetch service. When I saw the usage, I couldn’t find the detail information about the actual running state. So I investigated about it.

As the result, it was found that the fetchAll method is worked by the asynchronous processing. The returned data is reordered by the order of requests. By this, it was also found that if you want to retrieve the data from the several URL, the process cost of UrlFetchApp.fetchAll() is much lower than that of UrlFetchApp.fetch() using for loop.

Gists

Benchmark: Loop for Array Processing using Google Apps Script

Introduction

Please be careful! This result can be only used for Google Apps Script.

There are a limit executing time for Google Apps Script (GAS). That is 6 minutes.¹ So users always have to pay attention to reducing the process cost of the scripts. Especially, it is very important to know the process cost for the array processing, because the array processing is often used for spreadsheet and Google APIs. I have already reported “Improved Algorithms for Summation of Array Elements” as a method for reducing the process cost.² In this report, the process cost of “loop” for the array processing using GAS has been investigated.

Gists

Introduction

There are event objects at Google Apps Script. Typically, users which use Spreadsheet often use onEdit(event). Here, I would like to introduce the process costs for the event objects using this onEdit(event).

When onEdit(event) is used for the spreadsheet, event of onEdit(event) has the following structure.

{
  "authMode": {},
  "range": {
    "columnStart": 1,
    "rowStart": 1,
    "rowEnd": 1,
    "columnEnd": 1
  },
  "source": {},
  "oldValue": "old sample text",
  "user": {
    "nickname": "sampleName",
    "email": "sample email"
  },
  "value": "sample text"
}

In this structure, for example, the range of active cell is "range": {"columnStart": 1, "rowStart": 1, "rowEnd": 1, "columnEnd": 1 }. Namely, it’s “A1”. Users can use the range of active cell using this event object. In this report, I have investigated the process cost for retrieving the range of active cell as a sample.

This sample script is for retrieving values from a deep nested JSON. There are 2 patterns. So for these, the benchmark were measured.

Script :

package main

import (
    "encoding/json"
    "testing"
)

const (
    data = `{
      "A_key1": {
        "B_key1": {
          "C_key": "value"
        }
      }
    }`
)

func BenchmarkB1(b *testing.B) {
    b.ResetTimer()
    for i := 0; i < b.N; i++ {
        var p map[string]interface{}
        json.Unmarshal([]byte(data), &p)
        a1 := p["A_key1"]
        a2 := p["A_key1"].(map[string]interface{})["B_key1"]
        a3 := p["A_key1"].(map[string]interface{})["B_key1"].(map[string]interface{})["C_key"]
        _ = a1 // --> map[B_key1:map[C_key:value]]
        _ = a2 // --> map[C_key:value]
        _ = a3 // --> value
    }
}

func BenchmarkB2(b *testing.B) {
    b.ResetTimer()
    for i := 0; i < b.N; i++ {
        var p map[string]interface{}
        json.Unmarshal([]byte(data), &p)
        b1 := p["A_key1"]
        temp, _ := json.Marshal(b1)
        json.Unmarshal(temp, &p)
        b2 := p["B_key1"]
        temp, _ = json.Marshal(b2)
        json.Unmarshal(temp, &p)
        b3 := p["C_key"]
        _ = b1 // --> map[B_key1:map[C_key:value]]
        _ = b2 // --> map[C_key:value]
        _ = b3 // --> value
    }
}

Result :

$ go test -bench .
BenchmarkB1-4             300000              4177 ns/op
BenchmarkB2-4             100000             13619 ns/op
PASS

It was found that the process cost of json.Unmarshal() was high. json.Unmarshal() for test 2 is 3 times larger than that for test 1.

Description

There are a limit executing time for Google Apps Script (GAS). It’s 6 minutes. So users have to pay attention to the process cost of the script. GAS can use JavaScript 1.7. This means to be able to be used comprehension for GAS.

In this report, the process cost for the comprehension has been investigated. The normal for loop was used as the competitor. As a result, it was found that the comprehension can be used one of methods for reducing the process cost. For 1 dimensional array, the process cost of comprehension is about 60% lower than that of the normal one. For 2 dimensional array, the process cost of comprehension is about 50% lower than that of the normal one. Each data is the average for 10 times measurements.

This sample script is for splitting command-line arguments by golang. There are 2 types. One is the regular expression is used. Another is that Split() and TrimSpace() are used.

Here, each process speed was compared.

Script :

package main

import (
    "regexp"
    "strings"
    "testing"
)

func BenchmarkB1(b *testing.B) {
    str := "test1.txt, test2.txt"
    b.ResetTimer()
    for i := 0; i < b.N; i++ {
        ar := regexp.MustCompile(`\s*,\s*`).Split(str, -1)
        var result []string
        for _, x := range ar {
            result = append(result, x) // --> 'test.js', 'test2.py'
        }
        _ = result
    }
}

func BenchmarkB2(b *testing.B) {
    str := "test1.txt, test2.txt"
    b.ResetTimer()
    for i := 0; i < b.N; i++ {
        ar := strings.Split(str, ",")
        var result []string
        for _, x := range ar {
            result = append(result, strings.TrimSpace(x)) // --> 'test.js', 'test2.py'
        }
        _ = result
    }
}

Result :

$ go test -bench .
BenchmarkB1-4             100000             13048 ns/op
BenchmarkB2-4            3000000               399 ns/op
PASS

Just as expected, the regular expression was slow. And it’s much slower than that of Split() and TrimSpace().

This “souwapy” is a library for summing array elements with high speed by new algorithm (Pyramid method). The speed is faster than csv and panbdas module of python and v8 engine of node.js. The souwapy module is 2.3 and 3.1 times faster than csv and pandas module, respectively. This was really surprised me. It was found that the theory was correct.

At first, I have created this theory for Google Apps Script. But recently I had to use large data and output a csv file on python. So I made this library. Additionally, I had wanted to know how to public own library to PyPI before. This chance was good for me. If this library is helpful for other people, I’m glad.

Abstract

I have already reported that the pyramid method is one of very effectively algolithms for summing string elements in an array using Google Apps Script (GAS). This report describes the adaptability of the pyramid method to any languages except for GAS. c++ (g++), Go, Java, Javascript on Node.js, Python and Ruby were chosen as the sample languages. In those languages, there are languages which have the distinctive commands for summing the array elements. In this report, “+” operator as a standard command and a special command for each language were used. For c++ (g++), Javascript on Node.js and Python which have no distinctive commands for summing the array elements, only “+” operator was used. For others, both “+” operator and each special command such as “[]byte”, “StringBuilder” and “«” were used. For languages without the distinctive commands for summing, the pyramid method made us show some interesting phenomena. It was found that the pyramid method shows a good effect on only the specific language. It was found that “+” operator had been optimized for g++ and Node.js. “+” operator of Python was corresponding to theoretical results. This means that “+” operator of Python is not optimized. On the other hand, for languages with the distinctive commands for summing, it was found that the distinctive commands is incompatible to the pyramid method. These results made us show the possibility of visualization for the optimized codes.

Abstract

I considered an efficient algorithm for summation of array elements. All elements in an array are string. When those elements are summed using scripts, a standard method is to add each element in order. If the script is run without any optimize, the process becomes gradually sluggish, because the total amount of active data during the summation process is proportional to the square of the number of array elements. This leads directly to the high process-cost. Such phenomenon notably appears at Google Apps Script (GAS). This report says about the solution of this problem using a new algorithm of a pyramid method. The pyramid method achieves that the total amount of active data increases proportional to the linear of the number of array elements. By this, the processing time becomes much shorter than that of the process using the standard method. The pyramid method achieved the process-cost reduction of $99.7%$ compared with the standard method at GAS. I realized again that new discoveries are hidden into the familiar scenes of every-day life.