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The following environment variables are required:
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COIN - see lib/coins.py, must be a coin NAME
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NETWORK - see lib/coins.py, must be a coin NET
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DB_DIRECTORY - path to the database directory (if relative, to run script)
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USERNAME - the username the server will run as
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SERVER_MAIN - path to the server_main.py script (if relative, to run script)
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DAEMON_URL - the URL used to connect to the daemon. Should be of the form
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http://username:password@hostname:port/
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Alternatively you can specify DAEMON_USERNAME, DAEMON_PASSWORD,
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DAEMON_HOST and DAEMON_PORT. DAEMON_PORT is optional and
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will default appropriately for COIN.
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In addition either RPC_URL must be given as the full RPC URL for
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connecting to the daemon, or you must specify RPC_HOST, RPC_USER,
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RPC_PASSWORD and optionally RPC_PORT (it defaults appropriately for
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the coin and network otherwise).
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The other environment variables are all optional and will adopt
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sensible defaults if not specified.
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REORG_LIMIT - maximum number of blocks to be able to handle in a chain
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reorganisation. ElectrumX retains some fairly compact
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undo information for this many blocks in levelDB.
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Default is 200.
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Your performance might change by tweaking these cache settings. Cache
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size is only checked roughly every minute, so the caches can grow
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beyond the specified size. Also the Python process is often quite a
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bit bigger than the combine cache size, because of Python overhead and
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also because leveldb can consume quite a lot of memory during UTXO
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flushing. So these are rough numbers only:
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HIST_MB - amount of history cache, in MB, to retain before flushing to
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disk. Default is 250; probably no benefit being much larger
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as history is append-only and not searched.
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UTXO_MB - amount of UTXO and history cache, in MB, to retain before
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flushing to disk. Default is 1000. This may be too large
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for small boxes or too small for machines with lots of RAM.
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Larger caches generally perform better as there is
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significant searching of the UTXO cache during indexing.
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However, I don't see much benefit in my tests pushing this
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too high, and in fact performance begins to fall. My
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machine has 24GB RAM; the slow down is probably because of
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leveldb caching and Python GC effects. However this may be
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very dependent on hardware and you may have different
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results.
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