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sync_es: die when killed
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parent
d89f74893b
commit
87db2e9bae
29
sync_es.py
29
sync_es.py
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@ -24,6 +24,10 @@ database into es, at the expense of redoing a (small) amount of indexing.
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This uses multithreading so we don't have to block on socket io (both binlog
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reading and es POSTing). asyncio soon™
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This script will exit on any sort of exception, so you'll want to use your
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supervisor's restart functionality, e.g. Restart=failure in systemd, or
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the poor man's `while true; do sync_es.py; sleep 1; done` in tmux.
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"""
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from elasticsearch import Elasticsearch
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from elasticsearch.helpers import bulk, BulkIndexError
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@ -132,14 +136,31 @@ def delet_this(row, index_name):
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'_type': 'torrent',
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'_id': str(row['values']['id'])}
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# we could try to make this script robust to errors from es or mysql, but since
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# the only thing we can do is "clear state and retry", it's easier to leave
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# this to the supervisor. If we we carrying around heavier state in-process,
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# it'd be more worth it to handle errors ourselves.
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#
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# Apparently there's no setDefaultUncaughtExceptionHandler in threading, and
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# sys.excepthook is also broken, so this gives us the same
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# exit-if-anything-happens semantics.
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class ExitingThread(Thread):
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def run(self):
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try:
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self.run_happy()
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except:
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log.exception("something happened")
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# sys.exit only exits the thread, lame
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import os
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os._exit(1)
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class BinlogReader(Thread):
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class BinlogReader(ExitingThread):
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# write_buf is the Queue we communicate with
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def __init__(self, write_buf):
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Thread.__init__(self)
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self.write_buf = write_buf
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def run(self):
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def run_happy(self):
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with open(SAVE_LOC) as f:
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pos = json.load(f)
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@ -228,7 +249,7 @@ class BinlogReader(Thread):
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else:
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raise Exception(f"unknown table {s.table}")
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class EsPoster(Thread):
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class EsPoster(ExitingThread):
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# read_buf is the queue of stuff to bulk post
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def __init__(self, read_buf, chunk_size=1000, flush_interval=5):
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Thread.__init__(self)
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@ -236,7 +257,7 @@ class EsPoster(Thread):
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self.chunk_size = chunk_size
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self.flush_interval = flush_interval
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def run(self):
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def run_happy(self):
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es = Elasticsearch(timeout=30)
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last_save = time.time()
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