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import { client } from '$lib/contentfulClient';

export async function get({ params }) {
    const blog = await client.getEntries({
        'fields.slug': params.slug,
        content_type: 'blog-post'
    });

    if (blog?.items?.length > 0) {
        return {

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<script context="module">
    import { goto } from '$app/navigation';
    export async function load({ fetch, params }) {
        const url = `/${params.slug}.json`;
        const res = await fetch(url);
        if (res.ok) {
            const { blog } = await res.json();

            return {
                props: {

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<script context="module">
    export async function load({ url }) {
        return {
            props: {
                currentUrl: url.pathname,
            },
        };
    }
</script>

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import adapter from '@sveltejs/adapter-netlify';
/** @type {import('@sveltejs/kit').Config} */
const config = {
    kit: {
        adapter: adapter(),
        vite: {
            plugins: [
                {
                    name: 'watch-content',
                    configureServer(server) {

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import { writeFileSync } from 'fs';
import contentful from 'contentful';

const isDev = process.env.NODE_ENV === 'development';

export const client = contentful.createClient({
    accessToken: isDev ? import.meta.env.VITE_CONTENTFUL_PREVIEW_TOKEN : 
                         import.meta.env.VITE_CONTENTFUL_DELIVERY_API_TOKEN,
    space: import.meta.env.VITE_CONTENTFUL_SPACE_ID,
    host: isDev ? 'preview.contentful.com' : 'cdn.contentful.com'

eladroz

I'm now working on big data processing with Pandas at scale, as a lightweight alternative to Spark. Fortunately, the Apache Arrow project brings with it an excellent and very fast Parquet reader and writer.

With the current push to ARM in both personal computers and the data center, I was curious to check the performance of my code on ARM - running on AWS' homegrown Graviton2 processor. Their c6g instance types are 20% cheaper than the equivalent Intel-based c5's, while promising faster performance. If that's the future, why not start getting ready now?

While there are already Python wheels for NumPy and Pandas, there is no official build yet for PyArrow. There's a pull request in the works,

eladroz

#
# These settings are REALLY sensitive! do you feel lucky??
# Consult the documentation for etsy/statsd, but it doesn't have all the info.
#
# Notes:
#
# (a) When determining xFilesFactor, make sure the actual reporting rate would fill enough buckets,
# so that the downsampling won't result in null values.
#
# (b) Up to StatsD v0.5.0, counters are written in two places: