Python Data Optimizer API API ID: 12866

Your ultimate solution for reducing memory usage, cutting storage costs, and enhancing application performance—all without the need to rewrite your existing business logic. With a single API call, you receive a comprehensive savings report detailing the optimization in bytes, KB, MB, or GB.
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文档和设置
通过封装此 MCP 创建技能: https://mcp.zylalabs.com/mcp?apikey=YOUR_ZYLA_API_KEY

Unlock Significant Real-World Savings

  • Integer List: 80 bytes reduced to 20 bytes (75% reduction)
  • Float List: 40 bytes reduced to 20 bytes (50% reduction)
  • Sparse Matrix (88% zeros): 200 bytes reduced to 48 bytes (76% reduction)
  • Six Boolean Variables: 168 bytes reduced to 28 bytes (83% reduction)
  • Large 1GB File: Memory optimized to accessed bytes only (up to 99% reduction)
  • Cloud Costs: Monthly bill reduced from $100 to $25-$70 (30-75% savings)

About the Python Data Optimizer API

The Python Data Optimizer API, developed by Winter Hazel Inc., intelligently identifies inefficient data types within your Python code and databases, replacing them with the most compact, efficient types that accurately retain your data.

Input and Output Specifications

Input: Send Python data structures, code snippets, or database connection strings encapsulated in JSON format via an API call.

Output: Receive optimized data along with a detailed savings report showcasing the original size, optimized size, and total bytes saved.

Core Optimization Features: 12 Capabilities

  1. Downcast numeric types: e.g., convert float64 to float32, int64 to int8—saving up to 75% memory.
  2. Transform object columns to categorical: converting pandas object columns to category types can save 50-90% memory.
  3. Enforce specialized containers: optimally converts lists to deque and Counter for faster operations and reduced memory.
  4. Utilize native arrays for large lists: converting Python lists to typed arrays can save up to 75% memory.
  5. Convert strings to numbers: automatically detects and converts numeric strings to int or float.
  6. Transform lists to sets: eliminate duplicate storage on large lists exceeding 100 items.
  7. Convert lists to tuples: using immutable tuples can save approximately 40 bytes per structure.
  8. Automatically remove duplicates: maintains original order while removing duplicate values.
  9. Flatten nested structures: efficiently reduces deeply nested lists into flat arrays.
  10. Map loose variables to key-value pairs: organize scattered variables into structured dictionaries.
  11. Lock fixed data as immutable: identify constant data, such as country codes, and secure as tuples.
  12. Detect empty containers: eliminate empty lists, dicts, and sets that unnecessarily consume memory.

Advanced Analysis Features: 5 Capabilities

  1. Memory Leak Detector: scans your Python code to identify global structures that increase indefinitely, reporting line numbers and severity.
  2. Sparse Matrix Optimizer: compresses matrices with over 70% zeros into CSR format; for example, a 1000x1000 matrix can shrink from 8MB to under 1MB.
  3. Generator Expression Advisor: identifies list comprehensions loading entire datasets into memory, potentially saving up to 80MB on datasets with 10 million items.
  4. Bitwise Flag Optimizer: replaces six boolean variables (168 bytes) with one integer (28 bytes), achieving an 83% reduction.
  5. Memory Mapping Advisor: recommends techniques like mmap, numpy.memmap, and pandas chunksize for handling large files, saving up to 990MB on 1GB files.

Database Schema Advisor

This feature connects directly to your database and scans tables and columns for oversized data types, returning precise recommendations along with bytes saved per row:

  • PostgreSQL: Replace BIGINT with TINYINT, SMALLINT.
  • MySQL: Use INT, FLOAT, VARCHAR instead of BIGINT, DOUBLE, LONGTEXT.
  • SQLite: Opt for BLOB instead of TEXT for storing binary data.
  • MongoDB: Use Float instead of Double, TINYINT instead of Int64.
  • SQL Server: Implement INT, VARCHAR instead of BIGINT, NVARCHAR.
  • Amazon RDS: Supports both MySQL and PostgreSQL engines.
  • Google BigQuery: Use INT32, FLOAT32 instead of INT64, FLOAT64.

Common Use Cases

  1. Reducing monthly expenses on AWS and Google Cloud by optimizing database column types.
  2. Enhancing the performance of Python applications through numeric data type downcasting.
  3. Identifying potential memory leaks before they lead to application failures.
  4. Compressing sparse matrices effectively in machine learning tasks.
  5. Optimizing the processing of large files with memory mapping techniques.

API 文档

端点


优化单个Python数据结构并返回完整的节省报告,显示原始大小、优化后大小和节省的字节数


                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25666/optimize+data
                                                                            
                                                                        

优化数据 - 端点功能

对象 描述
data [必需] Python data structure to optimize in JSON format
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

data

API 示例响应

{"status":"success","result":{"optimized":[1000,2000,3000,4000,5000],"savings":{"original_type":"list","optimized_type":"array.h","original_size":40,"optimized_size":10,"saved":"30 bytes"},"type_detected":"list"}}

优化数据 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25666/optimize+data' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data": [1000, 2000, 3000, 4000, 5000]}'

    

在一次API调用中优化多个Python数据结构 发送数据结构列表并为每个数据结构接收一份节省报告 显示原始大小 优化大小和节省的字节数



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25670/optimize+batch
                                                                            
                                                                        

优化批处理 - 端点功能

对象 描述
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

request_body

API 示例响应

{"status":"success","results":[{"optimized":[1000,2000,3000],"savings":{"original_type":"list","optimized_type":"array.h","original_size":24,"optimized_size":6,"saved":"18 bytes"},"type_detected":"list"},{"optimized":[1.5,2.5,3.5],"savings":{"original_type":"list","optimized_type":"array.f","original_size":24,"optimized_size":12,"saved":"12 bytes"},"type_detected":"list"}]}

优化批处理 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25670/optimize+batch' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data_list": [[1000, 2000, 3000], [1.5, 2.5, 3.5]]}'

    

 优化Python数据中的结构模式。检测重复项,将列表转换为元组,移除空容器,并扁平化嵌套结构以减少内存使用。



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25671/optimize+structural
                                                                            
                                                                        

优化结构 - 端点功能

对象 描述
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

request_body

API 示例响应

{"original_type":"list","optimized_type":"list","pattern_applied":"duplicates_removed — list preserved","original_size":88,"optimized_size":88,"saved":"0 bytes","optimized":["hello","world","python"]}

优化结构 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25671/optimize+structural' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data": ["hello", "world", "hello", "python"]}'

    

在一次API调用中优化多个Python数据结构以识别结构模式。检测重复项,将列表转换为元组,移除空容器并扁平化嵌套结构



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25672/optimize+structural+batch
                                                                            
                                                                        

优化结构批处理 - 端点功能

对象 描述
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

request_body

API 示例响应

{"status":"success","results":[{"original_type":"list","optimized_type":"list","pattern_applied":"duplicates_removed — list preserved","original_size":88,"optimized_size":88,"saved":"0 bytes","optimized":["hello","world"]},{"original_type":"list","optimized_type":"tuple","pattern_applied":"list_to_tuple — immutable conversion","original_size":120,"optimized_size":80,"saved":"40 bytes","optimized":[1,2,3,4,5]}]}

优化结构批处理 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25672/optimize+structural+batch' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data_list": [["hello", "world", "hello"], [1, 2, 3, 4, 5]]}'

    

 检测稀疏矩阵,其零值超过70%,并将其压缩为CSR格式。一个1000x1000的矩阵从8MB缩小到不到1MB,在机器学习应用中节省了大量内存



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25673/optimize+sparse+matrix
                                                                            
                                                                        

优化稀疏矩阵 - 端点功能

对象 描述
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

request_body

API 示例响应

{"status":"optimized","sparsity_ratio":87.5,"original_format":"dense matrix","optimized_format":"CSR sparse matrix","original_size":128,"optimized_size":32,"saved":"96 bytes","non_zero_elements":2,"total_elements":16,"compressed_data":{"format":"CSR","shape":[4,4],"rows":[0,2],"cols":[3,1],"values":[5,3],"total_elements":16,"stored_elements":2}}

优化稀疏矩阵 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25673/optimize+sparse+matrix' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"matrix": [[0,0,0,5],[0,0,0,0],[0,3,0,0],[0,0,0,0]]}'

    

在单个API调用中优化多个稀疏矩阵 检测具有70%以上零值的矩阵并将其压缩为CSR格式 在机器学习应用中节省显著的内存



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25674/optimize+sparse+matrix+batch
                                                                            
                                                                        

优化稀疏矩阵批处理 - 端点功能

对象 描述
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

request_body

API 示例响应

{"status":"success","results":[{"status":"optimized","sparsity_ratio":87.5,"original_format":"dense matrix","optimized_format":"CSR sparse matrix","original_size":128,"optimized_size":32,"saved":"96 bytes","non_zero_elements":2,"total_elements":16,"compressed_data":{"format":"CSR","shape":[4,4],"rows":[0,2],"cols":[3,1],"values":[5,3],"total_elements":16,"stored_elements":2}},{"status":"not_sparse","sparsity_ratio":0.0,"message":"Matrix is only 0.0% sparse. Optimization not needed.","original_size":32}]}

优化稀疏矩阵批处理 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25674/optimize+sparse+matrix+batch' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"matrices": [[[0,0,0,5],[0,0,0,0],[0,3,0,0],[0,0,0,0]],[[1,2],[3,4]]]}'

    

扫描Python代码并检测从无限增长的全局结构中产生的内存泄漏。报告行号和严重级别——高、中和低——并提供修复每个泄漏的建议



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25691/analyze+memory+leak
                                                                            
                                                                        

分析内存泄漏 - 端点功能

对象 描述
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

request_body

API 示例响应

{"status":"success","total_leaks_found":2,"severity":{"high":1,"medium":1,"low":0},"leaks":[{"type":"global_list","variable":"logs","line":3,"description":"Global list that grows indefinitely","recommendation":"Move 'logs' inside a function or class. Clear it periodically."},{"type":"large_global","variable":"logs","line":1,"description":"Global list 'logs' detected","recommendation":"Consider moving 'logs' inside a function or use weak references."}],"summary":"Found 2 potential memory leaks. 1 high severity, 1 medium severity, 0 low severity."}

分析内存泄漏 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25691/analyze+memory+leak' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"code": "logs = []\ndef add_log(msg):\n    logs.append(msg)"}'

    

扫描Python代码中的列表推导式,这些推导式将整个数据集加载到内存中,并建议使用生成器表达式。通过一次处理一个项目,在1000万个项目的数据集中节省高达80MB的内存



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25692/generator+advisor
                                                                            
                                                                        

发电机顾问 - 端点功能

对象 描述
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

request_body

API 示例响应

{"status":"success","total_recommendations":3,"recommendations":[{"type":"list_comprehension","line":2,"description":"List comprehension detected — loads all data into memory at once.","recommendation":"Convert to generator expression by replacing [] with ().","example_before":"[x for x in data]","example_after":"(x for x in data)","memory_impact":"High — saves memory proportional to data size"},{"type":"function_with_list","line":3,"function":"sum","description":"List comprehension passed to sum() — unnecessary memory allocation.","recommendation":"Replace list comprehension with generator inside sum().","example_before":"sum([x for x in data])","example_after":"sum(x for x in data)","memory_impact":"Very High — generator never builds the full list in memory"},{"type":"list_comprehension","line":3,"description":"List comprehension detected — loads all data into memory at once.","recommendation":"Convert to generator expression by replacing [] with ().","example_before":"[x for x in data]","example_after":"(x for x in data)","memory_impact":"High — saves memory proportional to data size"}],"summary":"Found 3 opportunities to use generator expressions.","benefit":"Generator expressions process data one item at a time — saving significant memory on large datasets."}

发电机顾问 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25692/generator+advisor' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"code": "numbers = [1,2,3,4,5]\nsquares = [x * x for x in numbers]\ntotal = sum([x * x for x in range(1000000)])"}'

    

检测Python代码中的多个布尔变量,并建议将它们合并成一个使用位标志的整数。用28个字节替换6个使用168个字节的布尔值 — 减少了83%



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25693/bitwise+optimizer
                                                                            
                                                                        

位运算优化器 - 端点功能

对象 描述
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

request_body

API 示例响应

{"status":"success","total_recommendations":1,"recommendations":[{"type":"multiple_booleans","boolean_variables":["is_active","is_admin","is_verified","is_premium","has_access","can_edit"],"count":6,"description":"Found 6 boolean variables that can be combined into one integer.","recommendation":"Replace multiple boolean variables with a single bitwise integer flag.","example_before":"is_active = True\nis_admin = True\nis_verified = True","example_after":"\nclass Flags:\n    IS_ACTIVE = 1\n    IS_ADMIN = 2\n    IS_VERIFIED = 4\n    IS_PREMIUM = 8\n    HAS_ACCESS = 16\n    CAN_EDIT = 32\n\n# Set flags\nflags = 0\nflags |= Flags.IS_ACTIVE\nflags |= Flags.IS_ADMIN\n\n# Check flags\nis_set = bool(flags & Flags.IS_ACTIVE)\n","memory_impact":"Saves 140 bytes — replacing 6 booleans with 1 integer"}],"savings":{"boolean_count":6,"original_size":168,"optimized_size":28,"saved":"140 bytes"},"summary":"Found 1 opportunities to use bitwise flags.","benefit":"Bitwise flags store multiple boolean states in one integer — saving memory and improving performance."}

位运算优化器 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25693/bitwise+optimizer' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"code": "is_active = True\nis_admin = False\nis_verified = True\nis_premium = False\nhas_access = True\ncan_edit = False"}'

    

分析用于大文件操作的Python代码并建议内存映射技术。建议使用mmap、numpy.memmap和pandas的chunksize,以在1GB文件上节省最多990MB。



                                                                            
POST https://zylalabs.com/api/12866/python+data+optimizer+api/25694/memory+mapper
                                                                            
                                                                        

内存映射器 - 端点功能

对象 描述
请求体 [必需] Json

剩余免费测试请求:3 / 3。


输入参数

request_body

API 示例响应

{"status":"success","total_recommendations":3,"recommendations":[{"type":"file_open","line":3,"description":"File opened with standard open() — loads entire file into memory.","recommendation":"Use mmap for large files to avoid loading entire file into memory.","example_before":"\nwith open('large_file.bin', 'rb') as f:\n    data = f.read()\n","example_after":"\nimport mmap\nwith open('large_file.bin', 'rb') as f:\n    with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:\n        data = mm[0:1024]  # Read only what you need\n","memory_impact":"High — reads only required bytes instead of entire file"},{"type":"numpy_file_load","line":5,"description":"numpy.loadtxt() loads entire file into memory.","recommendation":"Use numpy.memmap for large arrays to avoid full file loading.","example_before":"\nimport numpy as np\ndata = np.loadtxt('large_array.txt')\n","example_after":"\nimport numpy as np\ndata = np.memmap('large_array.bin', dtype='float32', mode='r')\n","memory_impact":"Very High — numpy memmap reads only accessed portions"},{"type":"pandas_file_load","line":6,"description":"pandas.read_csv() loads entire file into memory.","recommendation":"Use chunksize parameter to process file in smaller pieces.","example_before":"\nimport pandas as pd\ndf = pd.read_csv('large_file.csv')\n","example_after":"\nimport pandas as pd\nfor chunk in pd.read_csv('large_file.csv', chunksize=10000):\n    process(chunk)  # Process one chunk at a time\n","memory_impact":"Very High — processes file in chunks instead of loading all at once"}],"summary":"Found 3 opportunities to use memory mapping.","benefit":"Memory mapping reads only the parts of a file you need — saving significant memory on large files.","best_for":["Files larger than 100 MB","Binary data files","Large numpy arrays","Large CSV or data files"]}

内存映射器 - 代码片段


curl --location --request POST 'https://zylalabs.com/api/12866/python+data+optimizer+api/25694/memory+mapper' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"code": "import numpy as np\nimport pandas as pd\nfile = open('large_data.bin', 'rb')\ndata = file.read()\narray = np.loadtxt('large_array.txt')\ndf = pd.read_csv('large_file.csv')"}'

    

API 访问密钥和身份验证

注册后,每个开发者都会被分配一个个人 API 访问密钥,这是一个唯一的字母和数字组合,用于访问我们的 API 端点。要使用 Python Data Optimizer API 进行身份验证,只需在 Authorization 标头中包含您的 bearer token。
标头
标头 描述
授权 [必需] 应为 Bearer access_key. 订阅后,请查看上方的"您的 API 访问密钥"。

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Python Data Optimizer API FAQs

优化数据端点返回一个JSON对象包含优化数据结构的详细节省报告 说明原始和优化的大小以及检测到的数据类型

响应中的关键字段包括“优化的”(优化后的数据结构) “节省”(一个包含“原始类型”“优化类型”“原始大小”“优化大小”和“节省”的对象)以及“检测到的类型”(识别的数据类型)

响应数据以JSON格式组织,其中包含一个“status”字段指示成功或失败,后面是一个“result”对象,包含优化后的数据和节省报告

优化数据端点提供有关优化数据结构的信息 原始和优化的大小 节省的内存量以及检测到的数据类型

用户可以通过发送不同的Python数据结构、代码片段或封装在JSON格式中的数据库连接字符串来定制他们的数据请求,从而根据他们的具体需求进行量身定制的优化

在回复中“original_size”表示优化前的数据大小“optimized_size”显示优化后的大小“saved”量化通过优化过程实现的内存减少

典型的用例包括减少Python应用程序的内存使用,优化数据库列类型以降低存储成本,识别内存泄漏,以及压缩稀疏矩阵以用于机器学习任务

数据的准确性通过智能识别和替换低效的数据类型得以保持,同时确保优化后的类型准确地表示原始数据,从而在优化过程中保持其完整性

该API可以优化各种Python数据结构,包括列表、数组、字典和稀疏矩阵。它还支持代码片段和数据库连接字符串,允许多种优化场景

API可以将深度嵌套的列表展平为扁平数组,从而减少复杂性和内存使用。此功能在优化机器学习任务中常见的数据结构时尤为有用

"type_detected"字段表示API在优化前识别的原始数据类型。这有助于用户理解处理的数据类型,并确保他们能够验证优化结果

是的,API支持针对各种数据库系统的优化,包括PostgreSQL、MySQL、SQLite、MongoDB和SQL Server。它为每个系统提供量身定制的建议,以提高数据存储效率

如果输入数据已经优化,API仍会返回一份节省报告,指出不需要进一步优化。这帮助用户确认他们现有数据结构的效率

API智能识别并替换低效的数据类型,同时确保优化后的类型准确表示原始数据。这个过程维护数据完整性并防止信息丢失

将列表转换为集合可以消除重复值,从而节省内存,而使用元组提供不变性,这可以使每个结构减少大约40个字节的内存使用。这两种转换都提高了数据效率

节省报告详细说明了原始大小、优化后的大小和节省的总字节数。用户可以使用这些信息来评估优化的有效性,并就数据管理做出明智的决策

一般常见问题

Zyla API Hub 就像一个大型 API 商店,您可以在一个地方找到数千个 API。我们还为所有 API 提供专门支持和实时监控。注册后,您可以选择要使用的 API。请记住,每个 API 都需要自己的订阅。但如果您订阅多个 API,您将为所有这些 API 使用相同的密钥,使事情变得更简单。
价格以 USD(美元)、EUR(欧元)、CAD(加元)、AUD(澳元)和 GBP(英镑)列出。我们接受所有主要的借记卡和信用卡。我们的支付系统使用最新的安全技术,由 Stripe 提供支持,Stripe 是世界上最可靠的支付公司之一。如果您在使用卡片付款时遇到任何问题,请通过 [email protected]

此外,如果您已经以这些货币中的任何一种(USD、EUR、CAD、AUD、GBP)拥有有效订阅,该货币将保留用于后续订阅。只要您没有任何有效订阅,您可以随时更改货币。
定价页面上显示的本地货币基于您 IP 地址的国家/地区,仅供参考。实际价格以 USD(美元)为单位。当您付款时,即使您在我们的网站上看到以本地货币显示的等值金额,您的卡片对账单上也会以美元显示费用。这意味着您不能直接使用本地货币付款。
有时,银行可能会因其欺诈保护设置而拒绝收费。我们建议您首先联系您的银行,检查他们是否阻止了我们的收费。此外,您可以访问账单门户并更改关联的卡片以进行付款。如果这些方法不起作用并且您需要进一步帮助,请通过 [email protected]
价格由月度或年度订阅决定,具体取决于所选计划。
API 调用根据成功请求从您的计划中扣除。每个计划都包含您每月可以进行的特定数量的调用。只有成功的调用(由状态 200 响应指示)才会计入您的总数。这确保失败或不完整的请求不会影响您的月度配额。
Zyla API Hub 采用月度订阅系统。您的计费周期将从您购买付费计划的那一天开始,并在下个月的同一日期续订。因此,如果您想避免未来的费用,请提前取消订阅。
要升级您当前的订阅计划,只需转到 API 的定价页面并选择您要升级到的计划。升级将立即生效,让您立即享受新计划的功能。请注意,您之前计划中的任何剩余调用都不会转移到新计划,因此在升级时请注意这一点。您将被收取新计划的全部金额。
要检查您本月剩余多少 API 调用,请参考响应标头中的 "X-Zyla-API-Calls-Monthly-Remaining" 字段。例如,如果您的计划允许每月 1,000 个请求,而您已使用 100 个,则响应标头中的此字段将显示 900 个剩余调用。
要查看您的计划允许的最大 API 请求数,请检查 "X-Zyla-RateLimit-Limit" 响应标头。例如,如果您的计划包括每月 1,000 个请求,此标头将显示 1,000。
"X-Zyla-RateLimit-Reset" 标头显示您的速率限制重置之前的秒数。这告诉您何时您的请求计数将重新开始。例如,如果它显示 3,600,则意味着还有 3,600 秒直到限制重置。
是的,您可以随时通过访问您的账户并在账单页面上选择取消选项来取消您的计划。请注意,升级、降级和取消会立即生效。此外,取消后,您将不再有权访问该服务,即使您的配额中还有剩余调用。
为了让您有机会在没有任何承诺的情况下体验我们的 API,我们提供 7 天免费试用,允许您免费进行最多 50 次 API 调用。此试用只能使用一次,因此我们建议将其应用于您最感兴趣的 API。虽然我们的大多数 API 都提供免费试用,但有些可能不提供。试用在 7 天后或您进行了 50 次请求后结束,以先发生者为准。如果您在试用期间达到 50 次请求限制,您需要"开始您的付费计划"以继续发出请求。您可以在个人资料中的订阅 -> 选择您订阅的 API -> 定价标签下找到"开始您的付费计划"按钮。或者,如果您在第 7 天之前不取消订阅,您的免费试用将结束,您的计划将自动计费,授予您访问计划中指定的所有 API 调用的权限。请记住这一点以避免不必要的费用。
7 天后,您将被收取试用期间订阅的计划的全额费用。因此,在试用期结束前取消很重要。因忘记及时取消而提出的退款请求不被接受。
当您订阅 API 免费试用时,您可以进行最多 50 次 API 调用。如果您希望超出此限制进行额外的 API 调用,API 将提示您执行"开始您的付费计划"。您可以在个人资料中的订阅 -> 选择您订阅的 API -> 定价标签下找到"开始您的付费计划"按钮。
付款订单在每月 20 日至 30 日之间处理。如果您在 20 日之前提交请求,您的付款将在此时间范围内处理。
您可以通过我们的聊天渠道联系我们以获得即时帮助。我们始终在线,时间为上午 8 点至下午 5 点(EST)。如果您在该时间之后联系我们,我们将尽快回复您。此外,您可以通过 [email protected]

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