Support writing large json base64 and string values

[ElizabethOkerio]

Issues

This pull request fixes #2845.

Description

This pull request introduces functionality to handle the writing of large string and byte array values. It accomplishes this by breaking down oversized strings and bytes into smaller chunks and directly writing them to the buffer.

The process involves first checking if the size of the string or byte array exceeds the buffer's capacity. If it does, the program iterates through the large string, dividing it into chunks of a predefined size. Each chunk is then written to the buffer until the entire string is successfully written.

Furthermore, special characters within the string are properly escaped to ensure accurate representation during the writing process.

Methods added:

1. WriteStringValueInChunks(ReadOnlySpan value)
2. WriteStringValueInChunksAsync
3. WriteByteValueInChunks(ReadOnlySpan value)
4. WriteByteValueInChunksAsync(ReadOnlyMemory value)

LOH allocations before these changes:
char[] in LOH:

byte[] in LOH from growing the writer buffer when calling WriteStringValue

LOH allocations after these changes:
No byte[] or char[] in the LOH that comes from ODataUtf8JsonWriter

Latency Before:
Bombarding https://localhost:7120/customers/ODataMessageWriter-Utf8JsonWriter-Async?count=10&largeFields=true for 30s using 30 connection(s)

[=================================================================================================================] 30s

Done!

Statistics Avg Stdev Max
Reqs/sec 23.70 69.93 1492.98
Latency 1.71s 0.87s 7.36s
Latency Distribution
50% 1.60s
75% 1.73s
90% 1.87s
95% 2.29s
99% 6.58s
HTTP codes:
1xx - 0, 2xx - 534, 3xx - 0, 4xx - 0, 5xx - 0
others - 2
Errors:
tls handshake timed out - 2
Throughput: 533.38MB/s

Latency After:
Bombarding https://localhost:7120/customers/ODataMessageWriter-Utf8JsonWriter-Async?count=10&largeFields=true for 30s using 30 connection(s)
[=================================================================================================================] 30s
Done!
Statistics Avg Stdev Max
Reqs/sec 26.07 44.40 429.41
Latency 1.41s 578.31ms 5.48s
Latency Distribution
50% 1.33s
75% 1.47s
90% 1.64s
95% 1.86s
99% 4.79s
HTTP codes:
1xx - 0, 2xx - 648, 3xx - 0, 4xx - 0, 5xx - 0
others - 0
Throughput: 651.77MB/s

With this changes, there is an improvement in latency and there are no allocations in the LOH.

Checklist (Uncheck if it is not completed)

• Test cases added
• Build and test with one-click build and test script passed

Additional work necessary

If documentation update is needed, please add "Docs Needed" label to the issue and provide details about the required document change in the issue.

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• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
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• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
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Why proper sizing of changes matters

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• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
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  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
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  • Exercising separation of concerns within the code changes.

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Total files changed: 3

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Why proper sizing of changes matters

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optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
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  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
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  • Exercising separation of concerns within the code changes.

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Why proper sizing of changes matters

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optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
    iterations.
  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
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  • Exercising separation of concerns within the code changes.

What can I do to optimize my changes

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How to interpret the change counts in git diff output

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Why proper sizing of changes matters

Optimal pull request sizes drive a better predictable PR flow as they strike a
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optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
    iterations.
  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
  • Solving big problems by dividing them in well contained, smaller problems.
  • Exercising separation of concerns within the code changes.

What can I do to optimize my changes

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Why proper sizing of changes matters

Optimal pull request sizes drive a better predictable PR flow as they strike a
balance between between PR complexity and PR review overhead. PRs within the
optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
    iterations.
  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
  • Solving big problems by dividing them in well contained, smaller problems.
  • Exercising separation of concerns within the code changes.

What can I do to optimize my changes

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  • Understand your typical change complexity, drive towards the desired complexity by adjusting the label mapping in your prquantifier.yaml context profile.
  • Only use the labels that matter to you, see context specification to customize your prquantifier.yaml context profile.
• Change your engineering behaviors
  • For PRs that fall outside of the desired spectrum, review the details and check if:
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    • Your PR only solves one particular issue. (For example, don't refactor and code new features in the same PR).

How to interpret the change counts in git diff output

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• One line was modified: +1 -1 (git diff doesn't know about modified, it will
  interpret that line like one addition plus one deletion)
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Label      : Medium
Size       : +165 -10
Percentile : 55%

Total files changed: 3

Change summary by file extension:
.cs : +164 -10
.csproj : +1 -0

Change counts above are quantified counts, based on the PullRequestQuantifier customizations.

Why proper sizing of changes matters

Optimal pull request sizes drive a better predictable PR flow as they strike a
balance between between PR complexity and PR review overhead. PRs within the
optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
    iterations.
  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
  • Solving big problems by dividing them in well contained, smaller problems.
  • Exercising separation of concerns within the code changes.

What can I do to optimize my changes

• Use the PullRequestQuantifier to quantify your PR accurately
  • Create a context profile for your repo using the context generator
  • Exclude files that are not necessary to be reviewed or do not increase the review complexity. Example: Autogenerated code, docs, project IDE setting files, binaries, etc. Check out the Excluded section from your prquantifier.yaml context profile.
  • Understand your typical change complexity, drive towards the desired complexity by adjusting the label mapping in your prquantifier.yaml context profile.
  • Only use the labels that matter to you, see context specification to customize your prquantifier.yaml context profile.
• Change your engineering behaviors
  • For PRs that fall outside of the desired spectrum, review the details and check if:
    • Your PR could be split in smaller, self-contained PRs instead
    • Your PR only solves one particular issue. (For example, don't refactor and code new features in the same PR).

How to interpret the change counts in git diff output

• One line was added: +1 -0
• One line was deleted: +0 -1
• One line was modified: +1 -1 (git diff doesn't know about modified, it will
  interpret that line like one addition plus one deletion)
• Change percentiles: Change characteristics (addition, deletion, modification)
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Label      : Medium
Size       : +165 -10
Percentile : 55%

Total files changed: 3

Change summary by file extension:
.cs : +164 -10
.csproj : +1 -0

Change counts above are quantified counts, based on the PullRequestQuantifier customizations.

Why proper sizing of changes matters

Optimal pull request sizes drive a better predictable PR flow as they strike a
balance between between PR complexity and PR review overhead. PRs within the
optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
    iterations.
  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
  • Solving big problems by dividing them in well contained, smaller problems.
  • Exercising separation of concerns within the code changes.

What can I do to optimize my changes

• Use the PullRequestQuantifier to quantify your PR accurately
  • Create a context profile for your repo using the context generator
  • Exclude files that are not necessary to be reviewed or do not increase the review complexity. Example: Autogenerated code, docs, project IDE setting files, binaries, etc. Check out the Excluded section from your prquantifier.yaml context profile.
  • Understand your typical change complexity, drive towards the desired complexity by adjusting the label mapping in your prquantifier.yaml context profile.
  • Only use the labels that matter to you, see context specification to customize your prquantifier.yaml context profile.
• Change your engineering behaviors
  • For PRs that fall outside of the desired spectrum, review the details and check if:
    • Your PR could be split in smaller, self-contained PRs instead
    • Your PR only solves one particular issue. (For example, don't refactor and code new features in the same PR).

How to interpret the change counts in git diff output

• One line was added: +1 -0
• One line was deleted: +0 -1
• One line was modified: +1 -1 (git diff doesn't know about modified, it will
  interpret that line like one addition plus one deletion)
• Change percentiles: Change characteristics (addition, deletion, modification)
  of this PR in relation to all other PRs within the repository.

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