Find & Replace within an Entire Directory or Git Repo with sed
If replacing within a directory:
grep -rl 'apples' /dir_to_search_under | xargs sed -i 's/apples/oranges/g'
Or, within an entire git
repository:
If replacing within a directory:
grep -rl 'apples' /dir_to_search_under | xargs sed -i 's/apples/oranges/g'
Or, within an entire git
repository:
""" | |
This gist shows how to run asyncio loop in a separate thread. | |
It could be useful if you want to mix sync and async code together. | |
Python 3.7+ | |
""" | |
import asyncio | |
from datetime import datetime | |
from threading import Thread | |
from typing import Tuple, List, Iterable |
import asyncio | |
from concurrent import futures | |
import functools | |
import inspect | |
import threading | |
from grpc import _server | |
def _loop_mgr(loop: asyncio.AbstractEventLoop): |
This is about documenting getting Linux running on the late 2016 and mid 2017 MPB's; the focus is mostly on the MacBookPro13,3 and MacBookPro14,3 (15inch models), but I try to make it relevant and provide information for MacBookPro13,1, MacBookPro13,2, MacBookPro14,1, and MacBookPro14,2 (13inch models) too. I'm currently using Fedora 27, but most the things should be valid for other recent distros even if the details differ. The kernel version is 4.14.x (after latest update).
The state of linux on the MBP (with particular focus on MacBookPro13,2) is also being tracked on https://github.com/Dunedan/mbp-2016-linux . And for Ubuntu users there are a couple tutorials (here and here) focused on that distro and the MacBook.
Note: For those who have followed these instructions ealier, and in particular for those who have had problems with the custom DSDT, modifying the DSDT is not necessary anymore - se
##Neo4j GraphGist - Enterprise Architectures: Real-time Graph Updates using Kafka Messaging
A recent Neo4j whitepaper describes how Monsanto is performing real-time updates on a 600M node Neo4j graph using Kafka to consume data extracted from a large Oracle Exadata instance.
This modern data architecture combines a fast, scalable messaging platform (Kafka) for low latency data provisioning and an enterprise graph database (Neo4j) for high performance, in-memory analytics & OLTP - creating new and powerful real-time graph analytics capabilities for your enterprise applications.
postgres: | |
image: postgres:9.4 | |
volumes: | |
- ./init.sql:/docker-entrypoint-initdb.d/init.sql |
from sqlalchemy.orm import sessionmaker, relationship, aliased | |
from sqlalchemy import cast, Integer, Text, Column, ForeignKey, literal, null | |
from sqlalchemy.sql import column, label | |
class Catalog(Base): | |
__tablename__ = 'catalog' | |
id = Column(String, primary_key=True) | |
parentid = Column(String, ForeignKey('catalog.id')) | |
name = Column(String) | |
parent = relationship("Catalog", remote_side=[id]) |