makmanalp

TLDR: well intentioned but ultimately unhelpful IMHO. Here's why:

1. It's easy to make a judgement about bad query plans based on an extremely biased sample: To give you a sense of the variety of queries we have: as of today there are over 180k unique query fingerprints at HubSpot. Let's ignore the trivial ones: about 18k unique query fingerprints do > 1000 queries/sec. To be sure, query planner bugs are real, and I'm currently fairly sure we've hit one here (details later) but of the total a miniscule amount is /truly/ (more on this later) query planner silliness.
2. By contrast, humans can be quite bad at figuring out what index a query needs and will compare dismally to the above success rate if they start doing FORCE INDEX on everything manually. I mess it up often. I see smart, competent, experienced engineers mess it up quite literally every day. People have attempted to codify rules for this exhaustively - every time I scroll through that page I

makmanalp

Note: these are pretty rough notes I made for my team on the fly as I was reading through some pages. Some could be mildly inaccurate but hopefully not terribly so. I might resort to convenient fiction & simplification sometimes.

My top contenders, mostly based on popularity / community etc:

• Angular
• Backbone
• React
• Ember

Mostly about MVC (or derivatives, MVP / MVVM).

makmanalp

import dask.dataframe as dd
from dask.dataframe.utils import make_meta
from dask.delayed import delayed
import pandas as pd

from itertools import chain


def get_stata_dask_meta(file_name, meta_chunksize=10000, *args, **kwargs):
    """Load up first bit of the file for type metadata info. We have to resort

makmanalp

---
- hosts: all
  sudo: no
  tasks:
    - shell: echo "Client= [$SSH_CLIENT] Sock= [$SSH_AUTH_SOCK]"
      register: myecho
    - debug: msg="{{myecho.stdout}}"
    - shell: ssh-add -l
      register: myecho
    - debug: msg="{{myecho.stdout}}"

makmanalp

license: mit
scrolling: yes

makmanalp

ads-0-backup-1552296000-l2zs8 backup INFO 2019/03/11 19:22:01 tablet prod_iad-1360915300 still has decreasing replication lag of 208.710618394 seconds, will continue waiting
ads-0-backup-1552296000-l2zs8 backup INFO 2019/03/11 19:24:01 tablet prod_iad-1360915300 has caught up on replication
ads-0-backup-1552296000-l2zs8 backup INFO 2019/03/11 19:24:01 (prod_iad-1360915300) checking health
ads-0-backup-1552296000-l2zs8 backup INFO 2019/03/11 19:24:06 (prod_iad-1360915300) succeeded 1 of 3 healthchecks
ads-0-backup-1552296000-l2zs8 backup INFO 2019/03/11 19:24:11 (prod_iad-1360915300) succeeded 2 of 3 healthchecks
ads-0-backup-1552296000-l2zs8 backup INFO 2019/03/11 19:24:16 (prod_iad-1360915300) succeeded 3 of 3 healthchecks
ads-0-backup-1552296000-l2zs8 backup INFO 2019/03/11 19:24:16 getting replication status for replicas
ads-0-backup-1552296000-l2zs8 backup INFO 2019/03/11 19:24:16 getting replication status for master
ads-0-backup-1552296000-l2zs8 backup INFO 2019/03/11 19:24:16 comparing GTIDSets for err

makmanalp

Summary:

When we run a rolling restart on our orchestrator statefulset, the node that is the previous master will get stuck in a crash loop.

Findings so far:

• The pod that gets stuck in a crash loop seems to be the node that used to be the master.
• When you delete the pod, it somehow gets out of the crash loop.
• Using pprof, Leo tracked the crash to within the martini web framework (used by orchestrator - Profile map, problem region), while writing the response.
• More specifically, the X-Forwarded-For header in the response (which is supposed to contain the IP addresses of each proxy the server goes through) seems to [accumulate](https://github.com/golang/go/blob/b5be877ba4318422547068b85c673639cd843b7d/src/net/http/httputil/

makmanalp

#!/usr/bin/env python

import requests

from ansible.module_utils.basic import AnsibleModule

import traceback
try:
    from urllib.parse import quote
except ImportError:

makmanalp

from collections import OrderedDict
from itertools import product


def rectangularize(data, keys):
    """Make sure there is a row in the dataset for each unique combination of
    values for the given keys.

    E.g. If your rows are:
        [

makmanalp

from sqlalchemy import Column, Integer, String, DateTime, Boolean
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import event
import datetime

Base = declarative_base()


def validate_int(instance, value, oldvalue, initiator):
    # Assigning a string to an Integer column will try to coerce it to the