交易延迟的主要原因是 LiveCycle PDF Generator 的工作原理。为了保持文档保真度，使用创建原始文档的任何本机软件以编程方式打开文档，然后将其转换为 PDF。
考虑增加应用程序服务器堆大小和 LiveCycle Document Max Inline Size。这里有两篇有用的文章： 堆大小调整和大型文档处理
The main reason behind the length of time behind the transaction is how LiveCycle PDF Generator works. In order to maintain document fidelity, the document is programmatically opened using whatever native software the original document was created in, and then converted to PDF.
Consider increasing your application server heap size, and LiveCycle Document Max Inline Size. Here are two helpful articles: Heap Size Tuning and Large Document Processing
我了解到 Adobe Live Cycle 在 Windows 中比 Solaris 中表现更好、更快。将文档转换为 PDF 始终是单线程，这最终会消耗更多资源。确保增加堆大小、超时，如果使用多个服务器进行负载平衡，效果会更好。
I learnt that Adobe live cycle perform better and faster in windows rather than Solaris. Conversion of Document to PDF is always single threaded which ends up consuming more resources. Make sure to increase the heap size, Timeouts and it would serve better if load balancing is done with multiple servers.
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交易延迟的主要原因是 LiveCycle PDF Generator 的工作原理。为了保持文档保真度，使用创建原始文档的任何本机软件以编程方式打开文档，然后将其转换为 PDF。
考虑增加应用程序服务器堆大小和 LiveCycle Document Max Inline Size。这里有两篇有用的文章：
堆大小调整和大型文档处理
The main reason behind the length of time behind the transaction is how LiveCycle PDF Generator works. In order to maintain document fidelity, the document is programmatically opened using whatever native software the original document was created in, and then converted to PDF.
Consider increasing your application server heap size, and LiveCycle Document Max Inline Size. Here are two helpful articles:
Heap Size Tuning and Large Document Processing
我了解到 Adobe Live Cycle 在 Windows 中比 Solaris 中表现更好、更快。将文档转换为 PDF 始终是单线程，这最终会消耗更多资源。确保增加堆大小、超时，如果使用多个服务器进行负载平衡，效果会更好。
I learnt that Adobe live cycle perform better and faster in windows rather than Solaris. Conversion of Document to PDF is always single threaded which ends up consuming more resources. Make sure to increase the heap size, Timeouts and it would serve better if load balancing is done with multiple servers.