jm + formats   9

S3 Inventory Adds Apache ORC output format and Amazon Athena Integration
Interesting to see Amazon are kind of putting their money behind ORC as a new public data interchange format with this.

Update: the Amazon senior PM for Athena and EMR says: 'Actually, we like both ORC and Parquet. Athena can process both ORC and Parquet, and teams can choose if they want to use either.' -- https://twitter.com/abysinha/status/932700622540849152
orc  formats  data  interchange  s3  athena  output 
21 days ago by jm
seriot.ch - Parsing JSON is a Minefield 💣
Crockford chose not to version [the] JSON definition: 'Probably the boldest design decision I made was to not put a version number on JSON so there is no mechanism for revising it. We are stuck with JSON: whatever it is in its current form, that’s it.' Yet JSON is defined in at least six different documents.


"Boldest". ffs. :facepalm:
bold  courage  json  parsing  coding  data  formats  interchange  fail  standards  confusion 
october 2016 by jm
Osso
"A modern standard for event-oriented data". Avro schema, events have time and type, schema is external and not part of the Avro stream.

'a modern standard for representing event-oriented data in high-throughput operational systems. It uses existing open standards for schema definition and serialization, but adds semantic meaning and definition to make integration between systems easy, while still being size- and processing-efficient.

An Osso event is largely use case agnostic, and can represent a log message, stack trace, metric sample, user action taken, ad display or click, generic HTTP event, or otherwise. Every event has a set of common fields as well as optional key/value attributes that are typically event type-specific.'
osso  events  schema  data  interchange  formats  cep  event-processing  architecture 
september 2016 by jm
UncertML
a conceptual model, with accompanying XML schema, that may be used to quantify and exchange complex uncertainties in data. The interoperable model can be used to describe uncertainty in a variety of ways including:

Samples
Statistics including mean, variance, standard deviation and quantile
Probability distributions including marginal and joint distributions and mixture models
via:conor  uncertainty  statistics  xml  formats 
january 2015 by jm
Standard Markdown
John Gruber’s canonical description of Markdown’s syntax does not specify the syntax unambiguously. In the absence of a spec, early implementers consulted the original Markdown.pl code to resolve these ambiguities. But Markdown.pl was quite buggy, and gave manifestly bad results in many cases, so it was not a satisfactory replacement for a spec.

Because there is no unambiguous spec, implementations have diverged considerably. As a result, users are often surprised to find that a document that renders one way on one system (say, a GitHub wiki) renders differently on another (say, converting to docbook using Pandoc). To make matters worse, because nothing in Markdown counts as a “syntax error,” the divergence often isn't discovered right away.

There's no standard test suite for Markdown; the unofficial MDTest is the closest thing we have. The only way to resolve Markdown ambiguities and inconsistencies is Babelmark, which compares the output of 20+ implementations of Markdown against each other to see if a consensus emerges.

We propose a standard, unambiguous syntax specification for Markdown, along with a suite of comprehensive tests to validate Markdown implementations against this specification. We believe this is necessary, even essential, for the future of Markdown.
writing  markdown  specs  standards  text  formats  html 
september 2014 by jm
FlatBuffers: Main Page
A new serialization format from Google's Android gaming team, supporting C++ and Java, open source under the ASL v2. Reasons to use it:
Access to serialized data without parsing/unpacking - What sets FlatBuffers apart is that it represents hierarchical data in a flat binary buffer in such a way that it can still be accessed directly without parsing/unpacking, while also still supporting data structure evolution (forwards/backwards compatibility).
Memory efficiency and speed - The only memory needed to access your data is that of the buffer. It requires 0 additional allocations. FlatBuffers is also very suitable for use with mmap (or streaming), requiring only part of the buffer to be in memory. Access is close to the speed of raw struct access with only one extra indirection (a kind of vtable) to allow for format evolution and optional fields. It is aimed at projects where spending time and space (many memory allocations) to be able to access or construct serialized data is undesirable, such as in games or any other performance sensitive applications. See the benchmarks for details.
Flexible - Optional fields means not only do you get great forwards and backwards compatibility (increasingly important for long-lived games: don't have to update all data with each new version!). It also means you have a lot of choice in what data you write and what data you don't, and how you design data structures.
Tiny code footprint - Small amounts of generated code, and just a single small header as the minimum dependency, which is very easy to integrate. Again, see the benchmark section for details.
Strongly typed - Errors happen at compile time rather than manually having to write repetitive and error prone run-time checks. Useful code can be generated for you.
Convenient to use - Generated C++ code allows for terse access & construction code. Then there's optional functionality for parsing schemas and JSON-like text representations at runtime efficiently if needed (faster and more memory efficient than other JSON parsers).


Looks nice, but it misses the language coverage of protobuf. Definitely more practical than capnproto.
c++  google  java  serialization  json  formats  protobuf  capnproto  storage  flatbuffers 
june 2014 by jm
Simple Binary Encoding
an OSI layer 6 presentation for encoding/decoding messages in binary format to support low-latency applications. [...] SBE follows a number of design principles to achieve this goal. By adhering to these design principles sometimes means features available in other codecs will not being offered. For example, many codecs allow strings to be encoded at any field position in a message; SBE only allows variable length fields, such as strings, as fields grouped at the end of a message.

The SBE reference implementation consists of a compiler that takes a message schema as input and then generates language specific stubs. The stubs are used to directly encode and decode messages from buffers. The SBE tool can also generate a binary representation of the schema that can be used for the on-the-fly decoding of messages in a dynamic environment, such as for a log viewer or network sniffer.

The design principles drive the implementation of a codec that ensures messages are streamed through memory without backtracking, copying, or unnecessary allocation. Memory access patterns should not be underestimated in the design of a high-performance application. Low-latency systems in any language especially need to consider all allocation to avoid the resulting issues in reclamation. This applies for both managed runtime and native languages. SBE is totally allocation free in all three language implementations.

The end result of applying these design principles is a codec that has ~25X greater throughput than Google Protocol Buffers (GPB) with very low and predictable latency. This has been observed in micro-benchmarks and real-world application use. A typical market data message can be encoded, or decoded, in ~25ns compared to ~1000ns for the same message with GPB on the same hardware. XML and FIX tag value messages are orders of magnitude slower again.

The sweet spot for SBE is as a codec for structured data that is mostly fixed size fields which are numbers, bitsets, enums, and arrays. While it does work for strings and blobs, many my find some of the restrictions a usability issue. These users would be better off with another codec more suited to string encoding.
sbe  encoding  protobuf  protocol-buffers  json  messages  messaging  binary  formats  low-latency  martin-thompson  xml 
may 2014 by jm
'Pickles & Spores: Improving Support for Distributed Programming in Scala
'Spores are "small units of possibly mobile functional behavior". They're a closure-like abstraction meant for use in distributed or concurrent environments. Spores provide a guarantee that the environment is effectively immutable, and safe to ship over the wire. Spores aim to give library authors some confidence in exposing functions (or, rather, spores) in public APIs for safe consumption in a distributed or concurrent environment.

The first part of the talk covers a simpler variant of spores as they are proposed for inclusion in Scala 2.11. The second part of the talk briefly introduces a current research project ongoing at EPFL which leverages Scala's type system to provide type constraints that give authors finer-grained control over spore capturing semantics. What's more, these type constraints can be composed during spore composition, so library authors are effectively able to propagate expert knowledge via these composable constraints.

The last part of the talk briefly covers Scala/Pickling, a fast new, open serialization framework.'
pickling  scala  presentations  spores  closures  fp  immutability  coding  distributed  distcomp  serialization  formats  network 
april 2014 by jm
Cap'n Proto
Cap’n Proto is an insanely fast data interchange format and capability-based RPC system. Think JSON, except binary. Or think Protocol Buffers, except faster. In fact, in benchmarks, Cap’n Proto is INFINITY TIMES faster than Protocol Buffers.


Basically, marshalling like writing an aligned C struct to the wire, QNX messaging protocol-style. Wasteful on space, but responds to this by suggesting compression (which is a fair point tbh). C++-only for now. I'm not seeing the same kind of support for optional data that protobufs has though. Overall I'm worried there's some useful features being omitted here...
serialization  formats  protobufs  capn-proto  protocols  coding  c++  rpc  qnx  messaging  compression  compatibility  interoperability  i14y 
april 2013 by jm

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