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wasm disaggregated-storage pushdown

WASM Threading parallelisation of pushdown operations:

  • Architectures storage:

    • Storage is underpowered (# of cores or speed is not very great); Low-end edge computing; Types of operations supported are limited;
    • Base layer and an intermediary (with more mem & compute power but with higher latency) that is doing client handling (buffering between the two);
  • Scale-out: handling 100k or 1000s of requests per second.

    • what is worthwhile and what budget there is to parallelize. Typically many of these operations in terms of pushdown are not parallelizable;
    • Many of the opreations are pushdown to a highly optimized specialized services for a particular operation (e.g. parsing, aggregation, filters, etc.);
  • CSV & Parquet

    • Parsing a CSV can be broken into chunks although the bottleneck is the disk; But parsing a Parquet file has more indexing built into it; Especially if doing aggregation can be split more easily and do parallelism on them;

Notes

  • Software fault isolation
  • Fassm project OSS
  • SQL queries + UDF → custom user impl; WASM user defined applications;
  • Wasmtime is used by 50% of respondents, up from 36% in 2022, followed by Wasmer (40%, up from 36%), Wasm3 (18%, up from 14%), WasmEdge (15%, up from 7%), and WebAssembly Micro Runtime (WAMR) (12%, up from 6%). Wazeroo is used by 12% and it was not asked about in previous surveys.
  • Wrap wasm sandbox into another container VM; Firecracker;
  • Create a market place of WASM plugins that can manipulate raw data. e.g. implement table joins targeting specific tradeoffs;
  • GSL is applied not MSR;

Resouces

GPU


TODO

  • WASI + WASIX
  • Parquet2 parallel read (deserialization)
  • Parquet2 parallel write (deserialization + compression) error: RuntimeError: unreachable;`
  • wasm64-unknown-unknown target for larger space x64
  • performance under docker
  • Intergrate benchmark implementations
  • scales not linearily with array size growth (marshelling and copying data); Individual processes on chucks of data; Show that there is a big gap between wasm and native execution;
  • analyse "bytecode", MT methodology,

memory

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