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For experimenting with some ideas on improving blockchain scaling :

https://github.com/justgord/blksimjs

Currently simulates the growth of a crypto-currency blockchain ( similar to Bitcoin ), loops thru a cycle of :

  • spend – pick addresses at random and spend to another address
  • mine – put waiting transactions into the block, mine the cryptographic hash, add to chain

Features / Limitations :

  • uses single sha256 bit hash for ids
  • uses an easy Proof-of-work [ 256 tries on average to get a block hashs with ’00’ leading bytes ]
  • uses node.js byte buffers for transactions and blocks
  • runs around 7000 tps on i5 laptop

Motivation

I wanted to simulate the growth of a blockchain with unspent transactions spread somewhat sparsely at the early older parts of the blockchain, and more dense at the top of the blockchain [as more recent transactions havent had time to be spent yet ].

The reason for this is to test the feasibility of reducing the size of the data needed to bootstrap a new node. eg. in Bitcoin the whole dataset is :

  • around 150GB of transactions [ 250Mn txns ]
  • utxo of around 2GB [ ~50Mn txns ]
  • so unspent ‘utxo’ set is around 20% of transactions

Bring UTXO set forward

We can use much less data [5x smaller ] when spinning up a new node, by bringing utxo set forward to nearer the front of the chain. The sim gathers old utxos and injects them into the blockchain in baches of ids, so they are stamped into the block at block creation.

These ‘utxo catchup sections’ are read when starting a new processing node – ie. it only needs a provable list of utxos, not the complete history of all spent transactions.

skip links [ todo ]

Using block extension areas, we can also include skip links to blocks much earlier in the chain – the process of walking back thru the links of transactions inputs and outputs all the way back to the initial ‘genesis’ block is like walking a DAG tree, as un-needed areas of the blockchain are skipped over.  These skip links are validated at block creation time by other nodes.

This is useful for clients which want to traverse the chain to make better proof of validity that SPV, and for nodes that use utxo bring forward above, so they can trace PoW to an arbitrary level back to the genesis block.

Setting the scene

  • the database is 30 Million rows and performance is getting slower
  • users want to search for stuff.. but don’t know what they want to search for
  • the web experience needs to be fast enough to feel “interactive
  • it needs to have an api which the mobile app developers can use.. so json

Questions / Observations

  • Do we even have ‘Big Data’ ?
    • 9GB data as CSV
    • 2.5GB when zipped
  • We could actually fit the data all into 16GB RAM ..
    • why doesn’t the database do that ?
  • What if we fully “invert” the data, so tag searches are fast :
    • data  : id -> tags   [ in named fields ]
    • index : tag -> ids
    • “inverted” when all tags are indexed
    • so, given a tag we can quickly get all the record ids it appears in

Stategy / Plan

  • RAM is fast, use plenty : enough for :
    • cached rows and
    • the full index
  • keep the index fully in RAM : all the time
  • vertical scaling might work

The pain, oh the pain

  • cassandra OK
    • but we don’t have a cluster.. + its 150MB java bloat/dependencies install
  • mysql innodb OK ..
    • but weak sql support + a large csv import died
  • redis OK .. but
    • hit 25GB RAM before dying
    • too much space to store long sets of integers
  • new respect for postgres :
    • great sql support
    • csv import “copy” : nice, simple, fast, reliable
    • up the buffers, and warm the cache with some queries
      • will bring in linux virtual ram, based on most recently used
      • directive to hold index or table in RAM would be nice
  • linux utils, ftw :
    • head tail sed split sort uniq wc
    • csvkit
    • perl -pe ‘s///g’ [ and regexes generally in vim, node.js, perl ]
    • bash shell

The epiphany

  • keep the fully inverted index in RAM, all the time
  • save space with a binary format
  • develop custom “index server”
    • custom binary file format
    • node.js is nice to work with : buffers, streams

Partial Enlightenment

  • SSDs are incredible
    • seek SSD ~0.1ms vs  HDD ~10ms  [ 100x ]
    • read SSD  150MB/s vs ~500MB/s [ 4x ]
    • readable comparison here
  • a middle way :
    • custom index held in RAM
    • data in a sturdy RDB [ postgres ]
    • trust SSD performance
  • json result set compresses well
    • 256 rows ~ 170k json ~ 25k gzipped [ 7x ! ]

Good Performance Numbers

  • running on a linode host with 2GB RAM
    • 1GB for postgres cache
    • 1GB for index tag table :
      • first 700 MB of the 2.3 GB binary index file
      • rest is blocks of ids [ per each tag ]
  • tag search on 30 Million records :
    • 25ms to get the first 1000 ids from the index table and index file
    • 150ms to fetch the records with those ids !
  • web front end :
    • search is fast enough to be “interactive
    • user sees 450ms roundtrip [ including 200ms ping ]
    • gzip the json brings 2.3s fetch down to 0.45s
    • feels nearly instantaneous

Moral of the story

  • SSDs are FAST.. but they are NEW and DB software will need time to catch up
  • NoSQL vs SQL just means more options, and more hybrid approaches
  • You can still scale vertical
    • if your data is big [100M rows | 50GB ]
    • but not BIG big [ 1Bn rows | 1TB ]
  • RAM is expensive on the server
    • because hosting companies can share it over many users
    • 64GB linode = $ 640/m vs 2GB linode = $20/mo
    • potentially save $600 per month
  • we will see more soft realtime web sites .. web with data will feel more interactive