From 537c0e160a629d112f6484116fd1ae1fb10323d0 Mon Sep 17 00:00:00 2001 From: Alan Bernstein Date: Tue, 15 May 2018 14:35:35 -0500 Subject: [PATCH] Address review comments --- docs/administration.md | 2 +- docs/faq.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/administration.md b/docs/administration.md index 894bc1ae6..073976711 100644 --- a/docs/administration.md +++ b/docs/administration.md @@ -28,7 +28,7 @@ Pilosa is a concurrent application written in Go and can take full advantage of #### Disk -Even though the main dataset is in memory Pilosa does back up to disk frequently. We recommend SSDs--especially if you have a write heavy application. +Even though the main dataset is in memory Pilosa does back up to disk frequently. We recommend SSDs—especially if you have a write heavy application. #### Network diff --git a/docs/faq.md b/docs/faq.md index 0e633046d..6d4e4e215 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -18,7 +18,7 @@ Pilosa is not a database in the traditional sense. While Pilosa does store data Pilosa sits on top of a data store or multiple data stores. -### How is Pilosa different than Elasticsearch since they are both indexes? +### How is Pilosa different from Elasticsearch since they are both indexes? Elasticsearch is a search engine based on Lucene, and is therefore very good at indexing and searching large volumes of unstructured text. As it matures, Elasticsearch has continued to move into the analytics space, but its core data object is still the "document". Pilosa is specifically designed to index structured data and improve query speed. By representing data as the relationship between objects, and then storing those relationships in bitmaps, Pilosa can very efficiently search and compare many millions of data points while still maintaining a small memory footprint.