Internet DRAFT - draft-ietf-quic-qlog-main-schema
draft-ietf-quic-qlog-main-schema
QUIC R. Marx, Ed.
Internet-Draft Akamai
Intended status: Standards Track L. Niccolini, Ed.
Expires: 17 August 2023 Meta
M. Seemann, Ed.
Protocol Labs
L. Pardue, Ed.
Cloudflare
13 February 2023
Main logging schema for qlog
draft-ietf-quic-qlog-main-schema-05
Abstract
This document describes a high-level schema for a standardized
logging format called qlog. This format allows easy sharing of data
and the creation of reusable visualization and debugging tools. The
high-level schema in this document is intended to be protocol-
agnostic. Separate documents specify how the format should be used
for specific protocol data. The schema is also format-agnostic, and
can be represented in for example JSON, csv or protobuf.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
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Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on 17 August 2023.
Copyright Notice
Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Provisions Relating to IETF Documents (https://trustee.ietf.org/
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Notational Conventions . . . . . . . . . . . . . . . . . 4
1.1.1. Schema definition . . . . . . . . . . . . . . . . . . 4
1.1.2. Serialization examples . . . . . . . . . . . . . . . 6
2. Design goals . . . . . . . . . . . . . . . . . . . . . . . . 6
3. The high level qlog schema . . . . . . . . . . . . . . . . . 6
3.1. Summary . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2. traces . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.3. Individual Trace containers . . . . . . . . . . . . . . . 9
3.3.1. Configuration . . . . . . . . . . . . . . . . . . . . 11
3.3.2. vantage_point . . . . . . . . . . . . . . . . . . . . 12
3.4. Field name semantics . . . . . . . . . . . . . . . . . . 14
3.4.1. Timestamps . . . . . . . . . . . . . . . . . . . . . 15
3.4.2. Category and Event Type . . . . . . . . . . . . . . . 17
3.4.3. Data . . . . . . . . . . . . . . . . . . . . . . . . 18
3.4.4. protocol_type . . . . . . . . . . . . . . . . . . . . 19
3.4.5. Triggers . . . . . . . . . . . . . . . . . . . . . . 20
3.4.6. group_id . . . . . . . . . . . . . . . . . . . . . . 20
3.4.7. common_fields . . . . . . . . . . . . . . . . . . . . 22
4. Guidelines for event definition documents . . . . . . . . . . 24
4.1. Event design guidelines . . . . . . . . . . . . . . . . . 24
4.2. Event importance indicators . . . . . . . . . . . . . . . 25
4.3. Custom fields . . . . . . . . . . . . . . . . . . . . . . 26
5. Generic events and data classes . . . . . . . . . . . . . . . 26
5.1. Raw packet and frame information . . . . . . . . . . . . 26
5.2. Generic events . . . . . . . . . . . . . . . . . . . . . 27
5.2.1. error . . . . . . . . . . . . . . . . . . . . . . . . 27
5.2.2. warning . . . . . . . . . . . . . . . . . . . . . . . 28
5.2.3. info . . . . . . . . . . . . . . . . . . . . . . . . 28
5.2.4. debug . . . . . . . . . . . . . . . . . . . . . . . . 28
5.2.5. verbose . . . . . . . . . . . . . . . . . . . . . . . 29
5.3. Simulation events . . . . . . . . . . . . . . . . . . . . 29
5.3.1. scenario . . . . . . . . . . . . . . . . . . . . . . 29
5.3.2. marker . . . . . . . . . . . . . . . . . . . . . . . 30
6. Serializing qlog . . . . . . . . . . . . . . . . . . . . . . 30
6.1. qlog to JSON mapping . . . . . . . . . . . . . . . . . . 31
6.1.1. I-JSON . . . . . . . . . . . . . . . . . . . . . . . 31
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6.1.2. Truncated values . . . . . . . . . . . . . . . . . . 32
6.2. qlog to JSON Text Sequences mapping . . . . . . . . . . . 33
6.2.1. Supporting JSON Text Sequences in tooling . . . . . . 36
6.3. Other optimized formatting options . . . . . . . . . . . 36
6.3.1. Data structure optimizations . . . . . . . . . . . . 37
6.3.2. Compression . . . . . . . . . . . . . . . . . . . . . 38
6.3.3. Binary formats . . . . . . . . . . . . . . . . . . . 39
6.3.4. Overview and summary . . . . . . . . . . . . . . . . 40
6.4. Conversion between formats . . . . . . . . . . . . . . . 41
7. Methods of access and generation . . . . . . . . . . . . . . 42
7.1. Set file output destination via an environment
variable . . . . . . . . . . . . . . . . . . . . . . . . 42
7.2. Access logs via a well-known endpoint . . . . . . . . . . 44
8. Tooling requirements . . . . . . . . . . . . . . . . . . . . 44
9. Security and privacy considerations . . . . . . . . . . . . . 45
10. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 47
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 47
11.1. Normative References . . . . . . . . . . . . . . . . . . 47
11.2. Informative References . . . . . . . . . . . . . . . . . 49
Appendix A. Change Log . . . . . . . . . . . . . . . . . . . . . 50
A.1. Since draft-ietf-quic-qlog-main-schema-04: . . . . . . . 50
A.2. Since draft-ietf-quic-qlog-main-schema-03: . . . . . . . 50
A.3. Since draft-ietf-quic-qlog-main-schema-02: . . . . . . . 50
A.4. Since draft-ietf-quic-qlog-main-schema-01: . . . . . . . 50
A.5. Since draft-ietf-quic-qlog-main-schema-00: . . . . . . . 50
A.6. Since draft-marx-qlog-main-schema-draft-02: . . . . . . . 50
A.7. Since draft-marx-qlog-main-schema-01: . . . . . . . . . . 51
A.8. Since draft-marx-qlog-main-schema-00: . . . . . . . . . . 51
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 51
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 52
1. Introduction
There is currently a lack of an easily usable, standardized endpoint
logging format. Especially for the use case of debugging and
evaluating modern Web protocols and their performance, it is often
difficult to obtain structured logs that provide adequate information
for tasks like problem root cause analysis.
This document aims to provide a high-level schema and harness that
describes the general layout of an easily usable, shareable,
aggregatable and structured logging format. This high-level schema
is protocol agnostic, with logging entries for specific protocols and
use cases being defined in other documents (see for example
[QLOG-QUIC] for QUIC and [QLOG-H3] for HTTP/3 and QPACK-related event
definitions).
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The goal of this high-level schema is to provide amenities and
default characteristics that each logging file should contain (or
should be able to contain), such that generic and reusable toolsets
can be created that can deal with logs from a variety of different
protocols and use cases.
As such, this document contains concepts such as versioning, metadata
inclusion, log aggregation, event grouping and log file size
reduction techniques.
The qlog schema can be serialized in many ways (e.g., JSON, CBOR,
protobuf, etc). This document describes only how to employ [JSON],
its subset [I-JSON], and its streamable derivative
[JSON-Text-Sequences].
Note to RFC editor: Please remove the follow paragraphs in this
section before publication.
Feedback and discussion are welcome at https://github.com/quicwg/qlog
(https://github.com/quicwg/qlog). Readers are advised to refer to
the "editor's draft" at that URL for an up-to-date version of this
document.
Concrete examples of integrations of this schema in various
programming languages can be found at https://github.com/quiclog/
qlog/ (https://github.com/quiclog/qlog/).
1.1. Notational Conventions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
1.1.1. Schema definition
To define events and data structures, all qlog documents use the
Concise Data Definition Language [CDDL]. This document uses the
basic syntax, the specific text, uint, float32, float64, bool, and
any types, as well as the .default, .size, and .regexp control
operators, the ~ unwrapping operator, and the $ extension point
syntax from [CDDL].
Additionally, this document defines the following custom types for
clarity:
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; CDDL's uint is defined as being 64-bit in size
; but for many protocol fields we want to be more restrictive
; and explicit
uint8 = uint .size 1
uint16 = uint .size 2
uint32 = uint .size 4
uint64 = uint .size 8
; an even-length lowercase string of hexadecimally encoded bytes
; examples: 82dc, 027339, 4cdbfd9bf0
; this is needed because the default CDDL binary string (bytes/bstr)
; is only CBOR and not JSON compatible
hexstring = text .regexp "([0-9a-f]{2})*"
Figure 1: Additional CDDL type definitions
All timestamps and time-related values (e.g., offsets) in qlog are
logged as float64 in the millisecond resolution.
Other qlog documents can define their own CDDL-compatible (struct)
types (e.g., separately for each Packet type that a protocol
supports).
Note to RFC editor: Please remove the following text in this
section before publication.
The main general CDDL syntax conventions in this document a reader
should be aware of for easy reading comprehension are:
* ? obj : this object is optional
* TypeName1 / TypeName2 : a union of these two types (object can be
either type 1 OR type 2)
* obj: TypeName : this object has this concrete type
* obj: [* TypeName] : this object is an array of this type with
minimum size of 0 elements
* obj: [+ TypeName] : this object is an array of this type with
minimum size of 1 element
* TypeName = ... : defines a new type
* EnumName = "entry1" / "entry2" / entry3 / ...: defines an enum
* StructName = { ... } : defines a new struct type
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* ; : single-line comment
* * text => any : special syntax to indicate 0 or more fields that
have a string key that maps to any value. Used to indicate a
generic JSON object.
All timestamps and time-related values (e.g., offsets) in qlog are
logged as float64 in the millisecond resolution.
Other qlog documents can define their own CDDL-compatible (struct)
types (e.g., separately for each Packet type that a protocol
supports).
1.1.2. Serialization examples
Serialization examples in this document use JSON ([JSON]) unless
otherwise indicated.
2. Design goals
The main tenets for the qlog schema design are:
* Streamable, event-based logging
* A flexible format that can reduce log producer overhead, at the
cost of increased complexity for consumers (e.g. tools)
* Extensible and pragmatic
* Aggregation and transformation friendly (e.g., the top-level
element for the non-streaming format is a container for individual
traces, group_ids can be used to tag events to a particular
context)
* Metadata is stored together with event data
3. The high level qlog schema
A qlog file should be able to contain several individual traces and
logs from multiple vantage points that are in some way related. To
that end, the top-level element in the qlog schema defines only a
small set of "header" fields and an array of component traces. For
this document, the required "qlog_version" field MUST have a value of
"0.3".
Note: there have been several previously broadly deployed qlog
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versions based on older drafts of this document (see draft-marx-
qlog-main-schema). The old values for the "qlog_version" field
were "draft-00", "draft-01" and "draft-02". When qlog was moved
to the QUIC working group, we decided to switch to a new
versioning scheme which is independent of individual draft
document numbers. However, we did start from 0.3, as conceptually
0.0, 0.1 and 0.2 can map to draft-00, draft-01 and draft-02.
As qlog can be serialized in a variety of ways, the "qlog_format"
field is used to indicate which serialization option was chosen. Its
value MUST either be one of the options defined in this document
(e.g., Section 6) or the field must be omitted entirely, in which
case it assumes the default value of "JSON".
In order to make it easier to parse and identify qlog files and their
serialization format, the "qlog_version" and "qlog_format" fields and
their values SHOULD be in the first 256 characters/bytes of the
resulting log file.
An example of the qlog file's top-level structure is shown in
Figure 2.
Definition:
QlogFile = {
qlog_version: text
? qlog_format: text .default "JSON"
? title: text
? description: text
? summary: Summary
? traces: [+ Trace / TraceError]
}
Figure 2: QlogFile definition
JSON serialization example:
{
"qlog_version": "0.3",
"qlog_format": "JSON",
"title": "Name of this particular qlog file (short)",
"description": "Description for this group of traces (long)",
"summary": {
...
},
"traces": [...]
}
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Figure 3: QlogFile example
3.1. Summary
In a real-life deployment with a large amount of generated logs, it
can be useful to sort and filter logs based on some basic summarized
or aggregated data (e.g., log length, packet loss rate, log location,
presence of error events, ...). The summary field (if present)
SHOULD be on top of the qlog file, as this allows for the file to be
processed in a streaming fashion (i.e., the implementation could just
read up to and including the summary field and then only load the
full logs that are deemed interesting by the user).
As the summary field is highly deployment-specific, this document
does not specify any default fields or their semantics. Some
examples of potential entries are shown in Section 3.1.
Definition:
Summary = {
; summary can contain any type of custom information
; text here doesn't mean the type text,
; but the fact that keys/names in the objects are strings
* text => any
}
Figure 4: Summary definition
JSON serialization example:
{
"trace_count": 1,
"max_duration": 5006,
"max_outgoing_loss_rate": 0.013,
"total_event_count": 568,
"error_count": 2
}
Figure 5: Summary example
3.2. traces
It is often advantageous to group several related qlog traces
together in a single file. For example, we can simultaneously
perform logging on the client, on the server and on a single point on
their common network path. For analysis, it is useful to aggregate
these three individual traces together into a single file, so it can
be uniquely stored, transferred and annotated.
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As such, the "traces" array contains a list of individual qlog
traces. Typical qlogs will only contain a single trace in this
array. These can later be combined into a single qlog file by taking
the "traces" entry/entries for each qlog file individually and
copying them to the "traces" array of a new, aggregated qlog file.
This is typically done in a post-processing step.
The "traces" array can thus contain both normal traces (for the
definition of the Trace type, see Section 3.3), but also "error"
entries. These indicate that we tried to find/convert a file for
inclusion in the aggregated qlog, but there was an error during the
process. Rather than silently dropping the erroneous file, we can
opt to explicitly include it in the qlog file as an entry in the
"traces" array, as shown in Figure 6.
Definition:
TraceError = {
error_description: text
; the original URI at which we attempted to find the file
? uri: text
? vantage_point: VantagePoint
}
Figure 6: TraceError definition
JSON serialization example:
{
"error_description": "File could not be found",
"uri": "/srv/traces/today/latest.qlog",
"vantage_point": { type: "server" }
}
Figure 7: TraceError example
Note that another way to combine events of different traces in a
single qlog file is through the use of the "group_id" field,
discussed in Section 3.4.6.
3.3. Individual Trace containers
The exact conceptual definition of a Trace can be fluid. For
example, a trace could contain all events for a single connection,
for a single endpoint, for a single measurement interval, for a
single protocol, etc. As such, a Trace container contains some
metadata in addition to the logged events, see Figure 8.
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In the normal use case however, a trace is a log of a single data
flow collected at a single location or vantage point. For example,
for QUIC, a single trace only contains events for a single logical
QUIC connection for either the client or the server.
The semantics and context of the trace can mainly be deduced from the
entries in the "common_fields" list and "vantage_point" field.
Definition:
Trace = {
? title: text
? description: text
? configuration: Configuration
? common_fields: CommonFields
? vantage_point: VantagePoint
events: [* Event]
}
Figure 8: Trace definition
JSON serialization example:
{
"title": "Name of this particular trace (short)",
"description": "Description for this trace (long)",
"configuration": {
"time_offset": 150
},
"common_fields": {
"ODCID": "abcde1234",
"time_format": "absolute"
},
"vantage_point": {
"name": "backend-67",
"type": "server"
},
"events": [...]
}
Figure 9: Trace example
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3.3.1. Configuration
We take into account that a qlog file is usually not used in
isolation, but by means of various tools. Especially when
aggregating various traces together or preparing traces for a
demonstration, one might wish to persist certain tool-based settings
inside the qlog file itself. For this, the configuration field is
used.
The configuration field can be viewed as a generic metadata field
that tools can fill with their own fields, based on per-tool logic.
It is best practice for tools to prefix each added field with their
tool name to prevent collisions across tools. This document only
defines two optional, standard, tool-independent configuration
settings: "time_offset" and "original_uris".
Definition:
Configuration = {
; time_offset is in milliseconds
time_offset: float64
original_uris:[* text]
* text => any
}
Figure 10: Configuration definition
JSON serialization example:
{
"time_offset": 150,
"original_uris": [
"https://example.org/trace1.qlog",
"https://example.org/trace2.qlog"
]
}
Figure 11: Configuration example
3.3.1.1. time_offset
The time_offset field indicates by how many milliseconds the starting
time of the current trace should be offset. This is useful when
comparing logs taken from various systems, where clocks might not be
perfectly synchronous. Users could use manual tools or automated
logic to align traces in time and the found optimal offsets can be
stored in this field for future usage. The default value is 0.
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3.3.1.2. original_uris
The original_uris field is used when merging multiple individual qlog
files or other source files (e.g., when converting .pcaps to qlog).
It allows to keep better track where certain data came from. It is a
simple array of strings. It is an array instead of a single string,
since a single qlog trace can be made up out of an aggregation of
multiple component qlog traces as well. The default value is an
empty array.
3.3.1.3. custom fields
Tools can add optional custom metadata to the "configuration" field
to store state and make it easier to share specific data viewpoints
and view configurations.
Two examples from the qvis toolset (https://qvis.edm.uhasselt.be) are
shown in Figure 12.
{
"configuration" : {
"qvis" : {
"congestion_graph": {
"startX": 1000,
"endX": 2000,
"focusOnEventIndex": 124
}
"sequence_diagram" : {
"focusOnEventIndex": 555
}
}
}
}
Figure 12: Custom configuration fields example
3.3.2. vantage_point
The vantage_point field describes the vantage point from which the
trace originates, see Figure 13. Each trace can have only a single
vantage_point and thus all events in a trace MUST BE from the
perspective of this vantage_point. To include events from multiple
vantage_points, implementers can for example include multiple traces,
split by vantage_point, in a single qlog file.
Definitions:
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VantagePoint = {
? name: text
type: VantagePointType
? flow: VantagePointType
}
; client = endpoint which initiates the connection
; server = endpoint which accepts the connection
; network = observer in between client and server
VantagePointType = "client" / "server" / "network" / "unknown"
Figure 13: VantagePoint definition
JSON serialization examples:
{
"name": "aioquic client",
"type": "client"
}
{
"name": "wireshark trace",
"type": "network",
"flow": "client"
}
Figure 14: VantagePoint example
The flow field is only required if the type is "network" (for
example, the trace is generated from a packet capture). It is used
to disambiguate events like "packet sent" and "packet received".
This is indicated explicitly because for multiple reasons (e.g.,
privacy) data from which the flow direction can be otherwise inferred
(e.g., IP addresses) might not be present in the logs.
Meaning of the different values for the flow field: * "client"
indicates that this vantage point follows client data flow semantics
(a "packet sent" event goes in the direction of the server). *
"server" indicates that this vantage point follow server data flow
semantics (a "packet sent" event goes in the direction of the
client). * "unknown" indicates that the flow's direction is unknown.
Depending on the context, tools confronted with "unknown" values in
the vantage_point can either try to heuristically infer the semantics
from protocol-level domain knowledge (e.g., in QUIC, the client
always sends the first packet) or give the user the option to switch
between client and server perspectives manually.
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3.4. Field name semantics
Inside of the "events" field of a qlog trace is a list of events
logged by the endpoint. Each event is specified as a generic object
with a number of member fields and their associated data. Depending
on the protocol and use case, the exact member field names and their
formats can differ across implementations. This section lists the
main, pre-defined and reserved field names with specific semantics
and expected corresponding value formats.
Each qlog event at minimum requires the "time" (Section 3.4.1),
"name" (Section 3.4.2) and "data" (Section 3.4.3) fields. Other
typical fields are "time_format" (Section 3.4.1), "protocol_type"
(Section 3.4.4), "trigger" (Section 3.4.5), and "group_id"
Section 3.4.6. As especially these later fields typically have
identical values across individual event instances, they are normally
logged separately in the "common_fields" (Section 3.4.7).
The specific values for each of these fields and their semantics are
defined in separate documents, specific per protocol or use case.
For example: event definitions for QUIC, HTTP/3 and QPACK can be
found in [QLOG-QUIC] and [QLOG-H3].
Other fields are explicitly allowed by the qlog approach, and tools
SHOULD allow for the presence of unknown event fields, but their
semantics depend on the context of the log usage (e.g., for QUIC, the
ODCID field is used), see [QLOG-QUIC].
An example of a qlog event with its component fields is shown in
Figure 15.
Definition:
Event = {
time: float64
name: text
data: $ProtocolEventBody
? time_format: TimeFormat
? protocol_type: ProtocolType
? group_id: GroupID
; events can contain any amount of custom fields
* text => any
}
Figure 15: Event definition
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JSON serialization:
{
"time": 1553986553572,
"name": "transport:packet_sent",
"data": { ... },
"protocol_type": ["QUIC","HTTP3"],
"group_id": "127ecc830d98f9d54a42c4f0842aa87e181a",
"time_format": "absolute",
"ODCID": "127ecc830d98f9d54a42c4f0842aa87e181a"
}
Figure 16: Event example
3.4.1. Timestamps
The "time" field indicates the timestamp at which the event occurred.
Its value is typically the Unix timestamp since the 1970 epoch
(number of milliseconds since midnight UTC, January 1, 1970, ignoring
leap seconds). However, qlog supports two more succinct timestamps
formats to allow reducing file size. The employed format is
indicated in the "time_format" field, which allows one of three
values: "absolute", "delta" or "relative".
Definition:
TimeFormat = "absolute" / "delta" / "relative"
Figure 17: TimeFormat definition
* Absolute: Include the full absolute timestamp with each event.
This approach uses the largest amount of characters. This is also
the default value of the "time_format" field.
* Delta: Delta-encode each time value on the previously logged
value. The first event in a trace typically logs the full
absolute timestamp. This approach uses the least amount of
characters.
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* Relative: Specify a full "reference_time" timestamp (typically
this is done up-front in "common_fields", see Section 3.4.7) and
include only relatively-encoded values based on this
reference_time with each event. The "reference_time" value is
typically the first absolute timestamp. This approach uses a
medium amount of characters.
The first option is good for stateless loggers, the second and third
for stateful loggers. The third option is generally preferred, since
it produces smaller files while being easier to reason about. An
example for each option can be seen in Figure 18.
The absolute approach will use:
1500, 1505, 1522, 1588
The delta approach will use:
1500, 5, 17, 66
The relative approach will:
- set the reference_time to 1500 in "common_fields"
- use: 0, 5, 22, 88
Figure 18: Three different approaches for logging timestamps
One of these options is typically chosen for the entire trace (put
differently: each event has the same value for the "time_format"
field). Each event MUST include a timestamp in the "time" field.
Events in each individual trace SHOULD be logged in strictly
ascending timestamp order (though not necessarily absolute value, for
the "delta" format). Tools CAN sort all events on the timestamp
before processing them, though are not required to (as this could
impose a significant processing overhead). This can be a problem
especially for multi-threaded and/or streaming loggers, who could
consider using a separate post-processor to order qlog events in time
if a tool do not provide this feature.
Timestamps do not have to use the UNIX epoch timestamp as their
reference. For example for privacy considerations, any initial
reference timestamps (for example "endpoint uptime in ms" or "time
since connection start in ms") can be chosen. Tools SHOULD NOT
assume the ability to derive the absolute Unix timestamp from qlog
traces, nor allow on them to relatively order events across two or
more separate traces (in this case, clock drift should also be taken
into account).
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3.4.2. Category and Event Type
Events differ mainly in the type of metadata associated with them.
To help identify a given event and how to interpret its metadata in
the "data" field (see Section 3.4.3), each event has an associated
"name" field. This can be considered as a concatenation of two other
fields, namely event "category" and event "type".
Category allows a higher-level grouping of events per specific event
type. For example for QUIC and HTTP/3, the different categories
could be "transport", "http", "qpack", and "recovery". Within these
categories, the event Type provides additional granularity. For
example for QUIC and HTTP/3, within the "transport" Category, there
would be "packet_sent" and "packet_received" events.
Logging category and type separately conceptually allows for fast and
high-level filtering based on category and the re-use of event types
across categories. However, it also considerably inflates the log
size and this flexibility is not used extensively in practice at the
time of writing.
As such, the default approach in qlog is to concatenate both field
values using the ":" character in the "name" field, as can be seen in
Figure 19. As such, qlog category and type names MUST NOT include
this character.
JSON serialization using separate fields:
{
"category": "transport",
"type": "packet_sent"
}
JSON serialization using ":" concatenated field:
{
"name": "transport:packet_sent"
}
Figure 19: Ways of logging category, type and name of an event.
Certain serializations CAN emit category and type as separate fields,
and qlog tools SHOULD be able to deal with both the concatenated
"name" field, and the separate "category" and "type" fields. Text-
based serializations however are encouraged to employ the
concatenated "name" field for efficiency.
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3.4.3. Data
The data field is a generic object. It contains the per-event
metadata and its form and semantics are defined per specific sort of
event. For example, data field value definitions for QUIC and HTTP/3
can be found in [QLOG-QUIC] and [QLOG-H3].
This field is defined here as a CDDL extension point (a "socket" or
"plug") named $ProtocolEventBody. Other documents MUST properly
extend this extension point when defining new data field content
options to enable automated validation of aggregated qlog schemas.
The only common field defined for the data field is the trigger
field, which is discussed in Section 3.4.5.
Definition:
; The ProtocolEventBody is any key-value map (e.g., JSON object)
; only the optional trigger field is defined in this document
$ProtocolEventBody /= {
? trigger: text
* text => any
}
; event documents are intended to extend this socket by using:
; NewProtocolEvents = EventType1 / EventType2 / ... / EventTypeN
; $ProtocolEventBody /= NewProtocolEvents
Figure 20: ProtocolEventBody definition
One purely illustrative example for a QUIC "packet_sent" event is
shown in Figure 21:
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TransportPacketSent = {
? packet_size: uint16
header: PacketHeader
? frames:[* QuicFrame]
? trigger: "pto_probe" / "retransmit_timeout" / "bandwidth_probe"
}
could be serialized as
{
"packet_size": 1280,
"header": {
"packet_type": "1RTT",
"packet_number": 123
},
"frames": [
{
"frame_type": "stream",
"length": 1000,
"offset": 456
},
{
"frame_type": "padding"
}
]
}
Figure 21: Example of the 'data' field for a QUIC packet_sent event
3.4.4. protocol_type
The "protocol_type" array field indicates to which protocols (or
protocol "stacks") this event belongs. This allows a single qlog
file to aggregate traces of different protocols (e.g., a web server
offering both TCP+HTTP/2 and QUIC+HTTP/3 connections).
Definition:
ProtocolType = [+ text]
Figure 22: ProtocolType definition
For example, QUIC and HTTP/3 events have the "QUIC" and "HTTP3"
protocol_type entry values, see [QLOG-QUIC] and [QLOG-H3].
Typically however, all events in a single trace are of the same few
protocols, and this array field is logged once in "common_fields",
see Section 3.4.7.
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3.4.5. Triggers
Sometimes, additional information is needed in the case where a
single event can be caused by a variety of other events. In the
normal case, the context of the surrounding log messages gives a hint
as to which of these other events was the cause. However, in highly-
parallel and optimized implementations, corresponding log messages
might separated in time. Another option is to explicitly indicate
these "triggers" in a high-level way per-event to get more fine-
grained information without much additional overhead.
In qlog, the optional "trigger" field contains a string value
describing the reason (if any) for this event instance occurring, see
Section 3.4.3. While this "trigger" field could be a property of the
qlog Event itself, it is instead a property of the "data" field
instead. This choice was made because many event types do not
include a trigger value, and having the field at the Event-level
would cause overhead in some serializations. Additional information
on the trigger can be added in the form of additional member fields
of the "data" field value, yet this is highly implementation-
specific, as are the trigger field's string values.
One purely illustrative example of some potential triggers for QUIC's
"packet_dropped" event is shown in Figure 23:
TransportPacketDropped = {
? packet_type: PacketType
? raw_length: uint16
? trigger: "key_unavailable" / "unknown_connection_id" /
"decrypt_error" / "unsupported_version"
}
Figure 23: Trigger example
3.4.6. group_id
As discussed in Section 3.3, a single qlog file can contain several
traces taken from different vantage points. However, a single trace
from one endpoint can also contain events from a variety of sources.
For example, a server implementation might choose to log events for
all incoming connections in a single large (streamed) qlog file. As
such, we need a method for splitting up events belonging to separate
logical entities.
The simplest way to perform this splitting is by associating a "group
identifier" to each event that indicates to which conceptual "group"
each event belongs. A post-processing step can then extract events
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per group. However, this group identifier can be highly protocol and
context-specific. In the example above, we might use QUIC's
"Original Destination Connection ID" to uniquely identify a
connection. As such, they might add a "ODCID" field to each event.
However, a middlebox logging IP or TCP traffic might rather use four-
tuples to identify connections, and add a "four_tuple" field.
As such, to provide consistency and ease of tooling in cross-protocol
and cross-context setups, qlog instead defines the common "group_id"
field, which contains a string value. Implementations are free to
use their preferred string serialization for this field, so long as
it contains a unique value per logical group. Some examples can be
seen in Figure 25.
Definition:
GroupID = text
Figure 24: GroupID definition
JSON serialization example for events grouped by four tuples and QUIC
connection IDs:
"events": [
{
"time": 1553986553579,
"protocol_type": ["TCP", "TLS", "HTTP2"],
"group_id": "ip1=2001:67c:1232:144:9498:6df6:f450:110b,
ip2=2001:67c:2b0:1c1::198,port1=59105,port2=80",
"name": "transport:packet_received",
"data": { ... }
},
{
"time": 1553986553581,
"protocol_type": ["QUIC","HTTP3"],
"group_id": "127ecc830d98f9d54a42c4f0842aa87e181a",
"name": "transport:packet_sent",
"data": { ... }
}
]
Figure 25: GroupID example
Note that in some contexts (for example a Multipath transport
protocol) it might make sense to add additional contextual per-event
fields (for example "path_id"), rather than use the group_id field
for that purpose.
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Note also that, typically, a single trace only contains events
belonging to a single logical group (for example, an individual QUIC
connection). As such, instead of logging the "group_id" field with
an identical value for each event instance, this field is typically
logged once in "common_fields", see Section 3.4.7.
3.4.7. common_fields
As discussed in the previous sections, information for a typical qlog
event varies in three main fields: "time", "name" and associated
data. Additionally, there are also several more advanced fields that
allow mixing events from different protocols and contexts inside of
the same trace (for example "protocol_type" and "group_id"). In most
"normal" use cases however, the values of these advanced fields are
consistent for each event instance (for example, a single trace
contains events for a single QUIC connection).
To reduce file size and making logging easier, qlog uses the
"common_fields" list to indicate those fields and their values that
are shared by all events in this component trace. This prevents
these fields from being logged for each individual event. An example
of this is shown in Figure 26.
JSON serialization with repeated field values
per-event instance:
{
"events": [{
"group_id": "127ecc830d98f9d54a42c4f0842aa87e181a",
"protocol_type": ["QUIC","HTTP3"],
"time_format": "relative",
"reference_time": 1553986553572,
"time": 2,
"name": "transport:packet_received",
"data": { ... }
},{
"group_id": "127ecc830d98f9d54a42c4f0842aa87e181a",
"protocol_type": ["QUIC","HTTP3"],
"time_format": "relative",
"reference_time": 1553986553572,
"time": 7,
"name": "http:frame_parsed",
"data": { ... }
}
]
}
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JSON serialization with repeated field values instead
extracted to common_fields:
{
"common_fields": {
"group_id": "127ecc830d98f9d54a42c4f0842aa87e181a",
"protocol_type": ["QUIC","HTTP3"],
"time_format": "relative",
"reference_time": 1553986553572
},
"events": [
{
"time": 2,
"name": "transport:packet_received",
"data": { ... }
},{
"time": 7,
"name": "http:frame_parsed",
"data": { ... }
}
]
}
Figure 26: CommonFields example
The "common_fields" field is a generic dictionary of key-value pairs,
where the key is always a string and the value can be of any type,
but is typically also a string or number. As such, unknown entries
in this dictionary MUST be disregarded by the user and tools (i.e.,
the presence of an unknown field is explicitly NOT an error).
The list of default qlog fields that are typically logged in
common_fields (as opposed to as individual fields per event instance)
are shown in the listing below:
Definition:
CommonFields = {
? time_format: TimeFormat
? reference_time: float64
? protocol_type: ProtocolType
? group_id: GroupID
* text => any
}
Figure 27: CommonFields definition
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Tools MUST be able to deal with these fields being defined either on
each event individually or combined in common_fields. Note that if
at least one event in a trace has a different value for a given
field, this field MUST NOT be added to common_fields but instead
defined on each event individually. Good example of such fields are
"time" and "data", who are divergent by nature.
4. Guidelines for event definition documents
This document only defines the main schema for the qlog format. This
is intended to be used together with specific, per-protocol event
definitions that specify the name (category + type) and data needed
for each individual event. This is with the intent to allow the qlog
main schema to be easily re-used for several protocols. Examples
include the QUIC event definitions [QLOG-QUIC] and HTTP/3 and QPACK
event definitions [QLOG-H3].
This section defines some basic annotations and concepts the creators
of event definition documents SHOULD follow to ensure a measure of
consistency, making it easier for qlog implementers to extrapolate
from one protocol to another.
4.1. Event design guidelines
TODO: pending QUIC working group discussion. This text reflects the
initial (qlog draft 01 and 02) setup.
There are several ways of defining qlog events. In practice, we have
seen two main types used so far: a) those that map directly to
concepts seen in the protocols (e.g., packet_sent) and b) those that
act as aggregating events that combine data from several possible
protocol behaviors or code paths into one (e.g., parameters_set).
The latter are typically used as a means to reduce the amount of
unique event definitions, as reflecting each possible protocol event
as a separate qlog entity would cause an explosion of event types.
Additionally, logging duplicate data is typically prevented as much
as possible. For example, packet header values that remain
consistent across many packets are split into separate events (for
example spin_bit_updated or connection_id_updated for QUIC).
Finally, we have typically refrained from adding additional state
change events if those state changes can be directly inferred from
data on the wire (for example flow control limit changes) if the
implementation is bug-free and spec-compliant. Exceptions have been
made for common events that benefit from being easily identifiable or
individually logged (for example packets_acked).
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4.2. Event importance indicators
Depending on how events are designed, it may be that several events
allow the logging of similar or overlapping data. For example the
separate QUIC connection_started event overlaps with the more generic
connection_state_updated. In these cases, it is not always clear
which event should be logged or used, and which event should take
precedence if e.g., both are present and provide conflicting
information.
To aid in this decision making, we recommend that each event SHOULD
have an "importance indicator" with one of three values, in
decreasing order of importance and expected usage:
* Core
* Base
* Extra
The "Core" events are the events that SHOULD be present in all qlog
files for a given protocol. These are typically tied to basic packet
and frame parsing and creation, as well as listing basic internal
metrics. Tool implementers SHOULD expect and add support for these
events, though SHOULD NOT expect all Core events to be present in
each qlog trace.
The "Base" events add additional debugging options and CAN be present
in qlog files. Most of these can be implicitly inferred from data in
Core events (if those contain all their properties), but for many it
is better to log the events explicitly as well, making it clearer how
the implementation behaves. These events are for example tied to
passing data around in buffers, to how internal state machines change
and help show when decisions are actually made based on received
data. Tool implementers SHOULD at least add support for showing the
contents of these events, if they do not handle them explicitly.
The "Extra" events are considered mostly useful for low-level
debugging of the implementation, rather than the protocol. They
allow more fine-grained tracking of internal behavior. As such, they
CAN be present in qlog files and tool implementers CAN add support
for these, but they are not required to.
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Note that in some cases, implementers might not want to log for
example data content details in the "Core" events due to performance
or privacy considerations. In this case, they SHOULD use (a subset
of) relevant "Base" events instead to ensure usability of the qlog
output. As an example, implementations that do not log QUIC
packet_received events and thus also not which (if any) ACK frames
the packet contains, SHOULD log packets_acked events instead.
Finally, for event types whose data (partially) overlap with other
event types' definitions, where necessary the event definition
document should include explicit guidance on which to use in specific
situations.
4.3. Custom fields
Event definition documents are free to define new category and event
types, top-level fields (e.g., a per-event field indicating its
privacy properties or path_id in multipath protocols), as well as
values for the "trigger" property within the "data" field, or other
member fields of the "data" field, as they see fit.
They however SHOULD NOT expect non-specialized tools to recognize or
visualize this custom data. However, tools SHOULD make an effort to
visualize even unknown data if possible in the specific tool's
context. If they do not, they MUST ignore these unknown fields.
5. Generic events and data classes
There are some event types and data classes that are common across
protocols, applications and use cases that benefit from being defined
in a single location. This section specifies such common
definitions.
5.1. Raw packet and frame information
While qlog is a high-level logging format, it also allows the
inclusion of most raw wire image information, such as byte lengths
and byte values. This is useful when for example investigating or
tuning packetization behavior or determining encoding/framing
overheads. However, these fields are not always necessary, can take
up considerable space, and can have a considerable privacy and
security impact (see Section 9). Where applicable, these fields are
grouped in a separate, optional, field named "raw" of type RawInfo.
The exact definition of entities, headers, trailers and payloads
depend on the protocol used.
Definition:
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RawInfo = {
; the full byte length of the entity (e.g., packet or frame),
; including possible headers and trailers
? length: uint64
; the byte length of the entity's payload,
; excluding possible headers or trailers
? payload_length: uint64
; the (potentially truncated) contents of the full entity,
; including headers and possibly trailers
? data: hexstring
}
Figure 28: RawInfo definition
The RawInfo:data field can be truncated for privacy or security
purposes, see Section 6.1.2. In this case, the length and
payload_length fields should still indicate the non-truncated lengths
when used for debugging purposes.
This document does not specify explicit header_length or
trailer_length fields. In protocols without trailers, header_length
can be calculated by subtracting the payload_length from the length.
In protocols with trailers (e.g., QUIC's AEAD tag), event definition
documents SHOULD define how to support header_length calculation.
5.2. Generic events
In typical logging setups, users utilize a discrete number of well-
defined logging categories, levels or severities to log freeform
(string) data. This generic events category replicates this approach
to allow implementations to fully replace their existing text-based
logging by qlog. This is done by providing events to log generic
strings for the typical well-known logging levels (error, warning,
info, debug, verbose).
For the events defined below, the "category" is "generic" and their
"type" is the name of the heading in lowercase (e.g., the "name" of
the error event is "generic:error").
5.2.1. error
Importance: Core
Used to log details of an internal error that might not get reflected
on the wire.
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Definition:
GenericError = {
? code: uint64
? message: text
}
Figure 29: GenericError definition
5.2.2. warning
Importance: Base
Used to log details of an internal warning that might not get
reflected on the wire.
Definition:
GenericWarning = {
? code: uint64
? message: text
}
Figure 30: GenericWarning definition
5.2.3. info
Importance: Extra
Used mainly for implementations that want to use qlog as their one
and only logging format but still want to support unstructured string
messages.
Definition:
GenericInfo = {
message: text
}
Figure 31: GenericInfo definition
5.2.4. debug
Importance: Extra
Used mainly for implementations that want to use qlog as their one
and only logging format but still want to support unstructured string
messages.
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Definition:
GenericDebug = {
message: text
}
Figure 32: GenericDebug definition
5.2.5. verbose
Importance: Extra
Used mainly for implementations that want to use qlog as their one
and only logging format but still want to support unstructured string
messages.
Definition:
GenericVerbose = {
message: text
}
Figure 33: GenericVerbose definition
5.3. Simulation events
When evaluating a protocol implementation, one typically sets up a
series of interoperability or benchmarking tests, in which the test
situations can change over time. For example, the network bandwidth
or latency can vary during the test, or the network can be fully
disable for a short time. In these setups, it is useful to know when
exactly these conditions are triggered, to allow for proper
correlation with other events.
For the events defined below, the "category" is "simulation" and
their "type" is the name of the heading in lowercase (e.g., the
"name" of the scenario event is "simulation:scenario").
5.3.1. scenario
Importance: Extra
Used to specify which specific scenario is being tested at this
particular instance. This could also be reflected in the top-level
qlog's summary or configuration fields, but having a separate event
allows easier aggregation of several simulations into one trace
(e.g., split by group_id).
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Definition:
SimulationScenario = {
? name: text
? details: {* text => any }
}
Figure 34: SimulationScenario definition
5.3.2. marker
Importance: Extra
Used to indicate when specific emulation conditions are triggered at
set times (e.g., at 3 seconds in 2% packet loss is introduced, at 10s
a NAT rebind is triggered).
Definition:
SimulationMarker = {
? type: text
? message: text
}
Figure 35: SimulationMarker definition
6. Serializing qlog
This document and other related qlog schema definitions are
intentionally serialization-format agnostic. This means that
implementers themselves can choose how to represent and serialize
qlog data practically on disk or on the wire. Some examples of
possible formats are JSON, CBOR, CSV, protocol buffers, flatbuffers,
etc.
All these formats make certain tradeoffs between flexibility and
efficiency, with textual formats like JSON typically being more
flexible but also less efficient than binary formats like protocol
buffers. The format choice will depend on the practical use case of
the qlog user. For example, for use in day to day debugging, a
plaintext readable (yet relatively large) format like JSON is
probably preferred. However, for use in production, a more optimized
yet restricted format can be better. In this latter case, it will be
more difficult to achieve interoperability between qlog
implementations of various protocol stacks, as some custom or tweaked
events from one might not be compatible with the format of the other.
This will also reflect in tooling: not all tools will support all
formats.
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This being said, the authors prefer JSON as the basis for storing
qlog, as it retains full flexibility and maximum interoperability.
Storage overhead can be managed well in practice by employing
compression. For this reason, this document details how to
practically transform qlog schema definitions to [JSON], its subset
[I-JSON], and its streamable derivative [JSON-Text-Sequences]s. We
discuss concrete options to bring down JSON size and processing
overheads in Section 6.3.
As depending on the employed format different deserializers/parsers
should be used, the "qlog_format" field is used to indicate the
chosen serialization approach. This field is always a string, but
can be made hierarchical by the use of the "." separator between
entries. For example, a value of "JSON.optimizationA" can indicate
that a default JSON format is being used, but that a certain
optimization of type A was applied to the file as well (see also
Section 6.3).
6.1. qlog to JSON mapping
When mapping qlog to normal JSON, the "qlog_format" field MUST have
the value "JSON". This is also the default qlog serialization and
default value of this field.
When using normal JSON serialization, the file extension/suffix
SHOULD be ".qlog" and the Media Type (if any) SHOULD be "application/
qlog+json" per [RFC6839].
JSON files by definition ([RFC8259]) MUST utilize the UTF-8 encoding,
both for the file itself and the string values.
While not specifically required by the JSON specification, all qlog
field names in a JSON serialization MUST be lowercase.
In order to serialize CDDL-based qlog event and data structure
definitions to JSON, the official CDDL-to-JSON mapping defined in
Appendix E of [CDDL] SHOULD be employed.
6.1.1. I-JSON
For some use cases, it should be taken into account that not all
popular JSON parsers support the full JSON format. Especially for
parsers integrated with the JavaScript programming language (e.g.,
Web browsers, NodeJS), users are recommended to stick to a JSON
subset dubbed [I-JSON] (or Internet-JSON).
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One of the key limitations of JavaScript and thus I-JSON is that it
cannot represent full 64-bit integers in standard operating mode
(i.e., without using BigInt extensions), instead being limited to the
range of [-(2**53)+1, (2**53)-1]. In these circumstances, Appendix E
of [CDDL] recommends defining new CDDL types for int64 and uint64
that limit their values to this range.
While this can be sensible and workable for most use cases, some
protocols targeting qlog serialization (e.g., QUIC, HTTP/3), might
require full uint64 variables in some (rare) circumstances. In these
situations, it should be allowed to also use the string-based
representation of uint64 values alongside the numerical
representation. Concretely, the following definition of uint64
should override the original and (web-based) tools should take into
account that a uint64 field can be either a number or string.
uint64 = text / uint .size 8
Figure 36: Custom uint64 definition for I-JSON
6.1.2. Truncated values
For some use cases (e.g., limiting file size, privacy), it can be
necessary not to log a full raw blob (using the hexstring type) but
instead a truncated value (for example, only the first 100 bytes of
an HTTP response body to be able to discern which file it actually
contained). In these cases, the original byte-size length cannot be
obtained from the serialized value directly.
As such, all qlog schema definitions SHOULD include a separate,
length-indicating field for all fields of type hexstring they
specify, see for example Section 5.1. This not only ensures the
original length can always be retrieved, but also allows the omission
of any raw value bytes of the field completely (e.g., out of privacy
or security considerations).
To reduce overhead however and in the case the full raw value is
logged, the extra length-indicating field can be left out. As such,
tools MUST be able to deal with this situation and derive the length
of the field from the raw value if no separate length-indicating
field is present. The main possible permutations are shown by
example in Figure 37.
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// both the full raw value and its length are present
// (length is redundant)
{
"raw_length": 5,
"raw": "051428abff"
}
// only the raw value is present, indicating it
// represents the fields full value the byte
// length is obtained by calculating raw.length / 2
{
"raw": "051428abff"
}
// only the length field is present, meaning the
// value was omitted
{
"raw_length": 5,
}
// both fields are present and the lengths do not match:
// the value was truncated to the first three bytes.
{
"raw_length": 5,
"raw": "051428"
}
Figure 37: Example for serializing truncated hexstrings
6.2. qlog to JSON Text Sequences mapping
One of the downsides of using pure JSON is that it is inherently a
non-streamable format. Put differently, it is not possible to simply
append new qlog events to a log file without "closing" this file at
the end by appending "]}]}". Without these closing tags, most JSON
parsers will be unable to parse the file entirely. As most platforms
do not provide a standard streaming JSON parser (which would be able
to deal with this problem), this document also provides a qlog
mapping to a streamable JSON format called JSON Text Sequences (JSON-
SEQ) ([RFC7464]).
When mapping qlog to JSON-SEQ, the "qlog_format" field MUST have the
value "JSON-SEQ".
When using JSON-SEQ serialization, the file extension/suffix SHOULD
be ".sqlog" (for "streaming" qlog) and the Media Type (if any) SHOULD
be "application/qlog+json-seq" per [RFC8091].
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JSON Text Sequences are very similar to JSON, except that JSON
objects are serialized as individual records, each prefixed by an
ASCII Record Separator (<RS>, 0x1E), and each ending with an ASCII
Line Feed character (\n, 0x0A). Note that each record can also
contain any amount of newlines in its body, as long as it ends with a
newline character before the next <RS> character.
Each qlog event is serialized and interpreted as an individual JSON
Text Sequence record, and can simply be appended as a new object at
the back of an event stream or log file. Put differently, unlike
default JSON, it does not require a file to be wrapped as a full
object with "{ ... }" or "[... ]".
For this to work, some qlog definitions have to be adjusted however.
Mainly, events are no longer part of the "events" array in the Trace
object, but are instead logged separately from the qlog "header", as
indicated by the TraceSeq object in Figure 38. Additionally, qlog's
JSON-SEQ mapping does not allow logging multiple individual traces in
a single qlog file. As such, the QlogFile:traces field is replaced
by the singular QlogFileSeq:trace field, see Figure 39. An example
can be seen in Figure 40. Note that the "group_id" field can still
be used on a per-event basis to include events from conceptually
different sources in a single JSON-SEQ qlog file.
Definition:
TraceSeq = {
? title: text
? description: text
? configuration: Configuration
? common_fields: CommonFields
? vantage_point: VantagePoint
}
Figure 38: TraceSeq definition
Definition:
QlogFileSeq = {
qlog_format: "JSON-SEQ"
qlog_version: text
? title: text
? description: text
? summary: Summary
trace: TraceSeq
}
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Figure 39: QlogFileSeq definition
JSON-SEQ serialization examples:
// list of qlog events, serialized in accordance with RFC 7464,
// starting with a Record Separator character and ending with a
// newline.
// For display purposes, Record Separators are rendered as <RS>
<RS>{
"qlog_version": "0.3",
"qlog_format": "JSON-SEQ",
"title": "Name of JSON Text Sequence qlog file (short)",
"description": "Description for this trace file (long)",
"summary": {
...
},
"trace": {
"common_fields": {
"protocol_type": ["QUIC","HTTP3"],
"group_id":"127ecc830d98f9d54a42c4f0842aa87e181a",
"time_format":"relative",
"reference_time": 1553986553572
},
"vantage_point": {
"name":"backend-67",
"type":"server"
}
}
}
<RS>{"time": 2, "name": "transport:parameters_set", "data": { ... } }
<RS>{"time": 7, "name": "transport:packet_sent", "data": { ... } }
...
Figure 40: Top-level element
Note: while not specifically required by the JSON-SEQ specification,
all qlog field names in a JSON-SEQ serialization MUST be lowercase.
In order to serialize all other CDDL-based qlog event and data
structure definitions to JSON-SEQ, the official CDDL-to-JSON mapping
defined in Appendix E of [CDDL] SHOULD still be employed.
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6.2.1. Supporting JSON Text Sequences in tooling
Note that JSON Text Sequences are not supported in most default
programming environments (unlike normal JSON). However, several
custom JSON-SEQ parsing libraries exist in most programming languages
that can be used and the format is easy enough to parse with existing
implementations (i.e., by splitting the file into its component
records and feeding them to a normal JSON parser individually, as
each record by itself is a valid JSON object).
6.3. Other optimized formatting options
Both the JSON and JSON-SEQ formatting options described above are
serviceable in general small to medium scale (debugging) setups.
However, these approaches tend to be relatively verbose, leading to
larger file sizes. Additionally, generalized JSON(-SEQ)
(de)serialization performance is typically (slightly) lower than that
of more optimized and predictable formats. Both aspects make these
formats more challenging (though still practical
(https://qlog.edm.uhasselt.be/anrw/)) to use in large scale setups.
During the development of qlog, we compared a multitude of
alternative formatting and optimization options. The results of this
study are summarized on the qlog github repository
(https://github.com/quiclog/internet-drafts/issues/30#issuecomment-
617675097). The rest of this section discusses some of these
approaches implementations could choose and the expected gains and
tradeoffs inherent therein. Tools SHOULD support mainly the
compression options listed in Section 6.3.2, as they provide the
largest wins for the least cost overall.
Over time, specific qlog formats and encodings can be created that
more formally define and combine some of the discussed optimizations
or add new ones. We choose to define these schemes in separate
documents to keep the main qlog definition clean and generalizable,
as not all contexts require the same performance or flexibility as
others and qlog is intended to be a broadly usable and extensible
format (for example more flexibility is needed in earlier stages of
protocol development, while more performance is typically needed in
later stages). This is also the main reason why the general qlog
format is the less optimized JSON instead of a more performant
option.
To be able to easily distinguish between these options in qlog
compatible tooling (without the need to have the user provide out-of-
band information or to (heuristically) parse and process files in a
multitude of ways, see also Section 8), we recommend using explicit
file extensions to indicate specific formats. As there are no
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standards in place for this type of extension to format mapping, we
employ a commonly used scheme here. Our approach is to list the
applied optimizations in the extension in ascending order of
application (e.g., if a qlog file is first optimized with technique A
and then compressed with technique B, the resulting file would have
the extension ".(s)qlog.A.B"). This allows tooling to start at the
back of the extension to "undo" applied optimizations to finally
arrive at the expected qlog representation.
6.3.1. Data structure optimizations
The first general category of optimizations is to alter the
representation of data within an JSON(-SEQ) qlog file to reduce file
size.
The first option is to employ a scheme similar to the CSV (comma
separated value [RFC4180]) format, which utilizes the concept of
column "headers" to prevent repeating field names for each datapoint
instance. Concretely for JSON qlog, several field names are repeated
with each event (i.e., time, name, data). These names could be
extracted into a separate list, after which qlog events could be
serialized as an array of values, as opposed to a full object. This
approach was a key part of the original qlog format (prior to draft-
02) using the "event_fields" field. However, tests showed that this
optimization only provided a mean file size reduction of 5% (100MB to
95MB) while significantly increasing the implementation complexity,
and this approach was abandoned in favor of the default JSON setup.
Implementations using this format should not employ a separate file
extension (as it still uses JSON), but rather employ a new value of
"JSON.namedheaders" (or "JSON-SEQ.namedheaders") for the
"qlog_format" field (see Section 3).
The second option is to replace field values and/or names with
indices into a (dynamic) lookup table. This is a common compression
technique and can provide significant file size reductions (up to 50%
in our tests, 100MB to 50MB). However, this approach is even more
difficult to implement efficiently and requires either including the
(dynamic) table in the resulting file (an approach taken by for
example Chromium's NetLog format
(https://www.chromium.org/developers/design-documents/network-stack/
netlog)) or defining a (static) table up-front and sharing this
between implementations. Implementations using this approach should
not employ a separate file extension (as it still uses JSON), but
rather employ a new value of "JSON.dictionary" (or "JSON-
SEQ.dictionary") for the "qlog_format" field (see Section 3).
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As both options either proved difficult to implement, reduced qlog
file readability, and provided too little improvement compared to
other more straightforward options (for example Section 6.3.2), these
schemes are not inherently part of qlog.
6.3.2. Compression
The second general category of optimizations is to utilize a
(generic) compression scheme for textual data. As qlog in the JSON(-
SEQ) format typically contains a large amount of repetition, off-the-
shelf (text) compression techniques typically succeed very well in
bringing down file sizes (regularly with up to two orders of
magnitude in our tests, even for "fast" compression levels). As
such, utilizing compression is recommended before attempting other
optimization options, even though this might (somewhat) increase
processing costs due to the additional compression step.
The first option is to use GZIP compression ([RFC1952]). This
generic compression scheme provides multiple compression levels
(providing a trade-off between compression speed and size reduction).
Utilized at level 6 (a medium setting thought to be applicable for
streaming compression of a qlog stream in commodity devices), gzip
compresses qlog JSON files to 7% of their initial size on average
(100MB to 7MB). For this option, the file extension .(s)qlog.gz
SHOULD BE used. The "qlog_format" field should still reflect the
original JSON formatting of the qlog data (e.g., "JSON" or "JSON-
SEQ").
The second option is to use Brotli compression ([RFC7932]). While
similar to gzip, this more recent compression scheme provides a
better efficiency. It also allows multiple compression levels.
Utilized at level 4 (a medium setting thought to be applicable for
streaming compression of a qlog stream in commodity devices), brotli
compresses qlog JSON files to 7% of their initial size on average
(100MB to 7MB). For this option, the file extension .(s)qlog.br
SHOULD BE used. The "qlog_format" field should still reflect the
original JSON formatting of the qlog data (e.g., "JSON" or "JSON-
SEQ").
Other compression algorithms of course exist (for example xz, zstd,
and lz4). We mainly recommend gzip and brotli because of their
tweakable behaviour and wide support in web-based environments, which
we envision as the main tooling ecosystem (see also Section 8).
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6.3.3. Binary formats
The third general category of optimizations is to use a more
optimized (often binary) format instead of the textual JSON format.
This approach inherently produces smaller files and often has better
(de)serialization performance. However, the resultant files are no
longer human readable and some formats require hard tradeoffs between
flexibility for performance.
The first option is to use the CBOR (Concise Binary Object
Representation [RFC7049]) format. For our purposes, CBOR can be
viewed as a straightforward binary variant of JSON. As such,
existing JSON qlog files can be trivially converted to and from CBOR
(though slightly more work is needed for JSON-SEQ qlogs to convert
them to CBOR-SEQ, see [RFC8742]). While CBOR thus does retain the
full qlog flexibility, it only provides a 25% file size reduction
(100MB to 75MB) compared to textual JSON(-SEQ). As CBOR support in
programming environments is not as widespread as that of textual JSON
and the format lacks human readability, CBOR was not chosen as the
default qlog format. For this option, the file extension
.(s)qlog.cbor SHOULD BE used. The "qlog_format" field should still
reflect the original JSON formatting of the qlog data (e.g., "JSON"
or "JSON-SEQ"). The media type should indicate both whether JSON or
JSON Text Sequences are used, as well as whether CBOR or CBOR
Sequences are used (see the table below).
A second option is to use a more specialized binary format, such as
Protocol Buffers (https://developers.google.com/protocol-buffers)
(protobuf). This format is battle-tested, has support for optional
fields and has libraries in most programming languages. Still, it is
significantly less flexible than textual JSON or CBOR, as it relies
on a separate, pre-defined schema (a .proto file). As such, it it
not possible to (easily) log new event types in protobuf files
without adjusting this schema as well, which has its own practical
challenges. As qlog is intended to be a flexible, general purpose
format, this type of format was not chosen as its basic
serialization. The lower flexibility does lead to significantly
reduced file sizes. Our straightforward mapping of the qlog main
schema and QUIC/HTTP3 event types to protobuf created qlog files 24%
as large as the raw JSON equivalents (100MB to 24MB). For this
option, the file extension .(s)qlog.protobuf SHOULD BE used. The
"qlog_format" field should reflect the different internal format, for
example: "qlog_format": "protobuf".
Note that binary formats can (and should) also be used in conjunction
with compression (see Section 6.3.2). For example, CBOR compresses
well (to about 6% of the original textual JSON size (100MB to 6MB)
for both gzip and brotli) and so does protobuf (5% (gzip) to 3%
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(brotli)). However, these gains are similar to the ones achieved by
simply compression the textual JSON equivalents directly (7%, see
Section 6.3.2). As such, since compression is still needed to
achieve optimal file size reductions event with binary formats, we
feel the more flexible compressed textual JSON options are a better
default for the qlog format in general.
6.3.4. Overview and summary
In summary, textual JSON was chosen as the main qlog format due to
its high flexibility and because its inefficiencies can be largely
solved by the utilization of compression techniques (which are needed
to achieve optimal results with other formats as well).
Still, qlog implementers are free to define other qlog formats
depending on their needs and context of use. These formats should be
described in their own documents, the discussion in this document
mainly acting as inspiration and high-level guidance. Implementers
are encouraged to add concrete qlog formats and definitions to the
designated public repository (https://github.com/quiclog/qlog).
The following table provides an overview of all the discussed qlog
formatting options with examples:
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+===============+===================+================+==============+
| format | qlog_format | extension | media type |
+===============+===================+================+==============+
| JSON | JSON | .qlog | application/ |
| Section 6.1 | | | qlog+json |
+---------------+-------------------+----------------+--------------+
| JSON Text | JSON-SEQ | .sqlog | application/ |
| Sequences | | | qlog+json- |
| Section 6.2 | | | seq |
+---------------+-------------------+----------------+--------------+
| named | JSON(- | .(s)qlog | application/ |
| headers | SEQ).namedheaders | | qlog+json(- |
| Section | | | seq) |
| 6.3.1 | | | |
+---------------+-------------------+----------------+--------------+
| dictionary | JSON(- | .(s)qlog | application/ |
| Section | SEQ).dictionary | | qlog+json(- |
| 6.3.1 | | | seq) |
+---------------+-------------------+----------------+--------------+
| CBOR | JSON(-SEQ) | .(s)qlog.cbor | application/ |
| Section | | | qlog+json(- |
| 6.3.3 | | | seq)+cbor(- |
| | | | seq) |
+---------------+-------------------+----------------+--------------+
| protobuf | protobuf | .qlog.protobuf | NOT |
| Section | | | SPECIFIED BY |
| 6.3.3 | | | IANA |
+---------------+-------------------+----------------+--------------+
+---------------+-------------------+----------------+--------------+
| gzip | no change | .gz suffix | application/ |
| Section | | | gzip |
| 6.3.2 | | | |
+---------------+-------------------+----------------+--------------+
| brotli | no change | .br suffix | NOT |
| Section | | | SPECIFIED BY |
| 6.3.2 | | | IANA |
+---------------+-------------------+----------------+--------------+
Table 1
6.4. Conversion between formats
As discussed in the previous sections, a qlog file can be serialized
in a multitude of formats, each of which can conceivably be
transformed into or from one another without loss of information.
For example, a number of JSON-SEQ streamed qlogs could be combined
into a JSON formatted qlog for later processing. Similarly, a
captured binary qlog could be transformed to JSON for easier
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interpretation and sharing.
Secondly, we can also consider other structured logging approaches
that contain similar (though typically not identical) data to qlog,
like raw packet capture files (for example .pcap files from tcpdump)
or endpoint-specific logging formats (for example the NetLog format
in Google Chrome). These are sometimes the only options, if an
implementation cannot or will not support direct qlog output for any
reason, but does provide other internal or external (e.g.,
SSLKEYLOGFILE export to allow decryption of packet captures) logging
options For this second category, a (partial) transformation from/to
qlog can also be defined.
As such, when defining a new qlog serialization format or wanting to
utilize qlog-compatible tools with existing codebases lacking qlog
support, it is recommended to define and provide a concrete mapping
from one format to default JSON-serialized qlog. Several of such
mappings exist. Firstly, [pcap2qlog]((https://github.com/quiclog/
pcap2qlog) transforms QUIC and HTTP/3 packet capture files to qlog.
Secondly, netlog2qlog
(https://github.com/quiclog/qvis/tree/master/visualizations/src/
components/filemanager/netlogconverter) converts chromium's internal
dictionary-encoded JSON format to qlog. Finally, quictrace2qlog
(https://github.com/quiclog/quictrace2qlog) converts the older
quictrace format to JSON qlog. Tools can then easily integrate with
these converters (either by incorporating them directly or for
example using them as a (web-based) API) so users can provide
different file types with ease. For example, the qvis
(https://qvis.edm.uhasselt.be) toolsuite supports a multitude of
formats and qlog serializations.
7. Methods of access and generation
Different implementations will have different ways of generating and
storing qlogs. However, there is still value in defining a few
default ways in which to steer this generation and access of the
results.
7.1. Set file output destination via an environment variable
To provide users control over where and how qlog files are created,
we define two environment variables. The first, QLOGFILE, indicates
a full path to where an individual qlog file should be stored. This
path MUST include the full file extension. The second, QLOGDIR, sets
a general directory path in which qlog files should be placed. This
path MUST include the directory separator character at the end.
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In general, QLOGDIR should be preferred over QLOGFILE if an endpoint
is prone to generate multiple qlog files. This can for example be
the case for a QUIC server implementation that logs each QUIC
connection in a separate qlog file. An alternative that uses
QLOGFILE would be a QUIC server that logs all connections in a single
file and uses the "group_id" field (Section 3.4.6) to allow post-hoc
separation of events.
Implementations SHOULD provide support for QLOGDIR and MAY provide
support for QLOGFILE.
When using QLOGDIR, it is up to the implementation to choose an
appropriate naming scheme for the qlog files themselves. The chosen
scheme will typically depend on the context or protocols used. For
example, for QUIC, it is recommended to use the Original Destination
Connection ID (ODCID), followed by the vantage point type of the
logging endpoint. Examples of all options for QUIC are shown in
Figure 41.
Command: QLOGFILE=/srv/qlogs/client.qlog quicclientbinary
Should result in the the quicclientbinary executable logging a
single qlog file named client.qlog in the /srv/qlogs directory.
This is for example useful in tests when the client sets up
just a single connection and then exits.
Command: QLOGDIR=/srv/qlogs/ quicserverbinary
Should result in the quicserverbinary executable generating
several logs files, one for each QUIC connection.
Given two QUIC connections, with ODCID values "abcde" and
"12345" respectively, this would result in two files:
/srv/qlogs/abcde_server.qlog
/srv/qlogs/12345_server.qlog
Command: QLOGFILE=/srv/qlogs/server.qlog quicserverbinary
Should result in the the quicserverbinary executable logging
a single qlog file named server.qlog in the /srv/qlogs directory.
Given that the server handled two QUIC connections before it was
shut down, with ODCID values "abcde" and "12345" respectively,
this would result in event instances in the qlog file being
tagged with the "group_id" field with values "abcde" and "12345".
Figure 41: Environment variable examples for a QUIC implementation
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7.2. Access logs via a well-known endpoint
After generation, qlog implementers MAY make available generated logs
and traces on an endpoint (typically the server) via the following
.well-known URI:
.well-known/qlog/IDENTIFIER.extension
The IDENTIFIER variable depends on the context and the protocol. For
example for QUIC, the lowercase Original Destination Connection ID
(ODCID) is recommended, as it can uniquely identify a connection.
Additionally, the extension depends on the chosen format (see
Section 6.3.4). For example, for a QUIC connection with ODCID
"abcde", the endpoint for fetching its default JSON-formatted .qlog
file would be:
.well-known/qlog/abcde.qlog
Implementers SHOULD allow users to fetch logs for a given connection
on a 2nd, separate connection. This helps prevent pollution of the
logs by fetching them over the same connection that one wishes to
observe through the log. Ideally, for the QUIC use case, the logs
should also be approachable via an HTTP/2 or HTTP/1.1 endpoint (i.e.,
on TCP port 443), to for example aid debugging in the case where
QUIC/UDP is blocked on the network.
qlog implementers SHOULD NOT enable this .well-known endpoint in
typical production settings to prevent (malicious) users from
downloading logs from other connections. Implementers are advised to
disable this endpoint by default and require specific actions from
the end users to enable it (and potentially qlog itself).
Implementers MUST also take into account the general privacy and
security guidelines discussed in Section 9 before exposing qlogs to
outside actors.
8. Tooling requirements
Tools ingestion qlog MUST indicate which qlog version(s), qlog
format(s), compression methods and potentially other input file
formats (for example .pcap) they support. Tools SHOULD at least
support .qlog files in the default JSON format (Section 6.1).
Additionally, they SHOULD indicate exactly which values for and
properties of the name (category and type) and data fields they look
for to execute their logic. Tools SHOULD perform a (high-level)
check if an input qlog file adheres to the expected qlog schema. If
a tool determines a qlog file does not contain enough supported
information to correctly execute the tool's logic, it SHOULD generate
a clear error message to this effect.
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Tools MUST NOT produce breaking errors for any field names and/or
values in the qlog format that they do not recognize. Tools SHOULD
indicate even unknown event occurrences within their context (e.g.,
marking unknown events on a timeline for manual interpretation by the
user).
Tool authors should be aware that, depending on the logging
implementation, some events will not always be present in all traces.
For example, using a circular logging buffer of a fixed size, it
could be that the earliest events (e.g., connection setup events) are
later overwritten by "newer" events. Alternatively, some events can
be intentionally omitted out of privacy or file size considerations.
Tool authors are encouraged to make their tools robust enough to
still provide adequate output for incomplete logs.
9. Security and privacy considerations
Protocols such as TLS [RFC8446] and QUIC [RFC9000] provide varying
degrees of secure protection for the wire image [RFC8546]. There is
inevitably tension between security and observability, when logging
can reveal aspects of the wire image, that would ordinarily be
protected. This tension equally applies to any privacy
considerations that build on security properties, especially if data
can be correlated across data sources.
qlog operators and implementers should be mindful of the security and
privacy risks inherent in handling qlog data. This includes but is
not limited to logging, storing, or using the data. Data might be
considered as non-sensitive, potentially-sensitive, or sensitive;
applying the considerations in this section may produce different
risks depending on the nature of the data itself, or its handling.
However, in many cases the largest risk factors arise from data that
can be considered as potenially-sensitive or sensitive.
The following is a non-exhaustive list of such fields and types of
data that can be carried in qlog data:
* IP addresses and transport protocol port numbers, which can be
used to uniquely identify individual connections, endpoints, and
potentially users.
* Session, Connection, or User identifiers which can be used to
correlate nominally separate contexts. For example, QUIC
Connection IDs can be used to identify and track users across
geographical networks Section 9.5 of [RFC9000]).
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* Stored State which can be used to correlate individual connections
or sessions over time. Examples include QUIC address validation
and retry tokens, TLS session tickets, and HTTP cookies.
* Decryption keys, passwords, and tokens which can be used with
other data sources (e.g., captures of encrypted packets) to
correlate qlog data to a specific connection or user or leak
additional information. Examples include TLS decryption keys and
HTTP-level API access or authorization tokens.
* Data that can be used to correlate qlogs to other data sources
(e.g., captures of encrypted packets). Examples include high-
resolution event timestamps or inter-event timings, event counts,
packet and frame sizes.
* Full or partial encrypted raw packet and frame payloads, which can
be used with other data sources (e.g., captures of encrypted
packets) to correlate qlog data to a specific connection or
session.
* Full or partial plaintext raw packet and frame payloads (e.g.,
HTTP Field values, HTTP response data, TLS SNI field values),
which can contain directly sensitive information.
The simplest and most extreme form of protection against abuse of
this information is the complete deletion of a given field, which is
equivalent to not logging the field(s) in question. While deletion
completely protects the data in the deleted fields from the risk of
compromise, it also reduces the utility of the dataset as a whole.
As such, a balance should be found between logging these fields and
the potential risks inherent in their (involuntary) disclosure. This
balance depends on the use case at hand (e.g., research datasets
might have different requirements to live operational
troubleshooting). Capturing the minimal amount of data required for
a specific purpose can help to minimize the risks associated with
data usage. qlog implementations that provide fine-grained control
over the inclusion of data fields, ideally on a per-use-case or per-
connection basis, improve the ability to minimize data.
Any data that is determined to be necessary for a use case at hand
could be logged or captured. As per [RFC6973], operators must be
aware that such data will be at risk of compromise. As such,
measures should be taken to firstly reduce the risk of compromise and
secondly reduce the risk of abuse of compromised data. While a full
discussion of both aspects is out of scope for this document, the
following paragraphs discuss high-level considerations that can be
applied to qlog data.
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To reduce the risk of compromise, operators can take measures such
as: limiting the length of time that data is stored, encrypting data
in transit and at rest, limiting access rights to the data, and
auditing data usage practices. qlog deployments that provide
integrated options for automated or manual data deletion and
(aggressive) aggregation, improve the ability to minimize the risk of
compromise.
To reduce the risk of data abuse after compromise, data can be
anonymized, pseudonymized, otherwise permutated/replaced, truncated,
(re-)encrypted, or aggregated. A partial discussion of applicable
techniques (especially for IP address information) can be found in
Appendix B of [DNS-PRIVACY]. Operators should, however, be aware
that many of these techniques have been shown to be insufficient to
safeguard user privacy and/or to protect user identity, especially if
a qlog data set is large or easily correlated against other data
sources.
Finally, qlog operators should consider the interplay between their
use case needs and end user rights or preferences. While active user
participation (as indicated by [RFC6973]) on a per-qlog basis is
difficult, as logs are often captured out-of-band to the main user
interaction and intent, general user expectations should be taken
into account. qlog deployments that provide mechanisms to integrate
the capture, storage and removal of qlogs with more general, often
pre-existing, user preference and privacy control systems, improve
the ability to protect data sensitive or confidential to the end
user. In qlog, these data are typically (but not exclusively)
contained in fields of the RawInfo type (see Section 5.1). qlog users
should thus be particularly hesitant to include these fields for all
but the most stringent use cases.
10. IANA Considerations
TODO: primarily the .well-known URI
11. References
11.1. Normative References
[CDDL] Birkholz, H., Vigano, C., and C. Bormann, "Concise Data
Definition Language (CDDL): A Notational Convention to
Express Concise Binary Object Representation (CBOR) and
JSON Data Structures", RFC 8610, DOI 10.17487/RFC8610,
June 2019, <https://www.rfc-editor.org/rfc/rfc8610>.
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[DNS-PRIVACY]
Dickinson, S., Overeinder, B., van Rijswijk-Deij, R., and
A. Mankin, "Recommendations for DNS Privacy Service
Operators", BCP 232, RFC 8932, DOI 10.17487/RFC8932,
October 2020, <https://www.rfc-editor.org/rfc/rfc8932>.
[I-JSON] Bray, T., Ed., "The I-JSON Message Format", RFC 7493,
DOI 10.17487/RFC7493, March 2015,
<https://www.rfc-editor.org/rfc/rfc7493>.
[JSON] Bray, T., Ed., "The JavaScript Object Notation (JSON) Data
Interchange Format", STD 90, RFC 8259,
DOI 10.17487/RFC8259, December 2017,
<https://www.rfc-editor.org/rfc/rfc8259>.
[JSON-Text-Sequences]
Williams, N., "JavaScript Object Notation (JSON) Text
Sequences", RFC 7464, DOI 10.17487/RFC7464, February 2015,
<https://www.rfc-editor.org/rfc/rfc7464>.
[RFC1952] Deutsch, P., "GZIP file format specification version 4.3",
RFC 1952, DOI 10.17487/RFC1952, May 1996,
<https://www.rfc-editor.org/rfc/rfc1952>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/rfc/rfc2119>.
[RFC4180] Shafranovich, Y., "Common Format and MIME Type for Comma-
Separated Values (CSV) Files", RFC 4180,
DOI 10.17487/RFC4180, October 2005,
<https://www.rfc-editor.org/rfc/rfc4180>.
[RFC6839] Hansen, T. and A. Melnikov, "Additional Media Type
Structured Syntax Suffixes", RFC 6839,
DOI 10.17487/RFC6839, January 2013,
<https://www.rfc-editor.org/rfc/rfc6839>.
[RFC6973] Cooper, A., Tschofenig, H., Aboba, B., Peterson, J.,
Morris, J., Hansen, M., and R. Smith, "Privacy
Considerations for Internet Protocols", RFC 6973,
DOI 10.17487/RFC6973, July 2013,
<https://www.rfc-editor.org/rfc/rfc6973>.
[RFC7049] Bormann, C. and P. Hoffman, "Concise Binary Object
Representation (CBOR)", RFC 7049, DOI 10.17487/RFC7049,
October 2013, <https://www.rfc-editor.org/rfc/rfc7049>.
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[RFC7464] Williams, N., "JavaScript Object Notation (JSON) Text
Sequences", RFC 7464, DOI 10.17487/RFC7464, February 2015,
<https://www.rfc-editor.org/rfc/rfc7464>.
[RFC7932] Alakuijala, J. and Z. Szabadka, "Brotli Compressed Data
Format", RFC 7932, DOI 10.17487/RFC7932, July 2016,
<https://www.rfc-editor.org/rfc/rfc7932>.
[RFC8091] Wilde, E., "A Media Type Structured Syntax Suffix for JSON
Text Sequences", RFC 8091, DOI 10.17487/RFC8091, February
2017, <https://www.rfc-editor.org/rfc/rfc8091>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.
[RFC8259] Bray, T., Ed., "The JavaScript Object Notation (JSON) Data
Interchange Format", STD 90, RFC 8259,
DOI 10.17487/RFC8259, December 2017,
<https://www.rfc-editor.org/rfc/rfc8259>.
[RFC8446] Rescorla, E., "The Transport Layer Security (TLS) Protocol
Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,
<https://www.rfc-editor.org/rfc/rfc8446>.
[RFC9000] Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
Multiplexed and Secure Transport", RFC 9000,
DOI 10.17487/RFC9000, May 2021,
<https://www.rfc-editor.org/rfc/rfc9000>.
11.2. Informative References
[QLOG-H3] Marx, R., Niccolini, L., Seemann, M., and L. Pardue,
"HTTP/3 and QPACK qlog event definitions", Work in
Progress, Internet-Draft, draft-ietf-quic-qlog-h3-events-
03, 24 October 2022,
<https://datatracker.ietf.org/doc/html/draft-ietf-quic-
qlog-h3-events-03>.
[QLOG-QUIC]
Marx, R., Niccolini, L., Seemann, M., and L. Pardue, "QUIC
event definitions for qlog", Work in Progress, Internet-
Draft, draft-ietf-quic-qlog-quic-events-03, 24 October
2022, <https://datatracker.ietf.org/doc/html/draft-ietf-
quic-qlog-quic-events-03>.
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[RFC8546] Trammell, B. and M. Kuehlewind, "The Wire Image of a
Network Protocol", RFC 8546, DOI 10.17487/RFC8546, April
2019, <https://www.rfc-editor.org/rfc/rfc8546>.
[RFC8742] Bormann, C., "Concise Binary Object Representation (CBOR)
Sequences", RFC 8742, DOI 10.17487/RFC8742, February 2020,
<https://www.rfc-editor.org/rfc/rfc8742>.
Appendix A. Change Log
A.1. Since draft-ietf-quic-qlog-main-schema-04:
* Updated RawInfo definition and guidance (#243)
A.2. Since draft-ietf-quic-qlog-main-schema-03:
* Added security and privacy considerations discussion (#252)
A.3. Since draft-ietf-quic-qlog-main-schema-02:
* No changes - new draft to prevent expiration
A.4. Since draft-ietf-quic-qlog-main-schema-01:
* Change the data definition language from TypeScript to CDDL (#143)
A.5. Since draft-ietf-quic-qlog-main-schema-00:
* Changed the streaming serialization format from NDJSON to JSON
Text Sequences (#172)
* Added Media Type definitions for various qlog formats (#158)
* Changed to semantic versioning
A.6. Since draft-marx-qlog-main-schema-draft-02:
* These changes were done in preparation of the adoption of the
drafts by the QUIC working group (#137)
* Moved RawInfo, Importance, Generic events and Simulation events to
this document.
* Added basic event definition guidelines
* Made protocol_type an array instead of a string (#146)
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A.7. Since draft-marx-qlog-main-schema-01:
* Decoupled qlog from the JSON format and described a mapping
instead (#89)
- Data types are now specified in this document and proper
definitions for fields were added in this format
- 64-bit numbers can now be either strings or numbers, with a
preference for numbers (#10)
- binary blobs are now logged as lowercase hex strings (#39, #36)
- added guidance to add length-specifiers for binary blobs (#102)
* Removed "time_units" from Configuration. All times are now in ms
instead (#95)
* Removed the "event_fields" setup for a more straightforward JSON
format (#101,#89)
* Added a streaming option using the NDJSON format (#109,#2,#106)
* Described optional optimization options for implementers (#30)
* Added QLOGDIR and QLOGFILE environment variables, clarified the
.well-known URL usage (#26,#33,#51)
* Overall tightened up the text and added more examples
A.8. Since draft-marx-qlog-main-schema-00:
* All field names are now lowercase (e.g., category instead of
CATEGORY)
* Triggers are now properties on the "data" field value, instead of
separate field types (#23)
* group_ids in common_fields is now just also group_id
Acknowledgements
Much of the initial work by Robin Marx was done at the Hasselt and KU
Leuven Universities.
Thanks to Jana Iyengar, Brian Trammell, Dmitri Tikhonov, Stephen
Petrides, Jari Arkko, Marcus Ihlar, Victor Vasiliev, Mirja
Kuehlewind, and Jeremy Laine for their feedback and suggestions.
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Authors' Addresses
Robin Marx (editor)
Akamai
Email: rmarx@akamai.com
Luca Niccolini (editor)
Meta
Email: lniccolini@meta.com
Marten Seemann (editor)
Protocol Labs
Email: marten@protocol.ai
Lucas Pardue (editor)
Cloudflare
Email: lucaspardue.24.7@gmail.com
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