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In this article, we will continue to explain how different components in Milvus interact with each other to complete real-time data queries.
Some useful resources before getting started are listed below. We recommend reading them first to better understand the topic in this post.
This is my series of articles covering short "today I learned" topics as I work with CockroachDB. Read the previous installments:
CockroachDB has a very user-friendly and helpful UI called DB Console. I like to refer to it when I debug query performance. There is a very useful Statements page that shows a statement overview and explains plans and execution stats.
In this tutorial we will see how to:
Imagine that you want to create a MuleSoft API to search products inside a catalog. This API will need to accept multiple filter parameters and an optional sorting parameter. All these parameters can be optional and will be used to generate a dynamic query to search products in your catalog database.
In previous blog articles, we have successively introduced the deletion, bitset, and compaction functions in Milvus 2.0. To culminate this series, we would like to share the design behind load balance, a vital function in the distributed cluster of Milvus.
Milvus 2.0 supports automatic load balance by default. But you can still trigger load balance manually. Please note that only sealed segments can be transferred across query nodes.
Read part 1 Migrating From Sakila-MySQL to Couchbase - ETL
A View is a virtual table built out of the result of a SQL statement.
Today, search is an important functionality in enterprise applications and end-users are obsessed with the experience of Google Search and expecting the application search also provides similar experiences.
This requires us to design and implement a search engine along with your golden source (RDBMS/NOSQL). There are many search engines available in the market today like Elasticsearch, Apache Solr, Azure Cognitive Search, etc. These provide a better search experience and features like typeahead, fuzzy search, boosting the search results based on relevancy, similarity search, etc.
Presto has evolved into a unified SQL engine on top of cloud data lakes for both interactive queries as well as batch workloads with multiple data sources. This tutorial will show how to run SQL queries with Presto (running on Kubernetes) on AWS Redshift.
Presto's Redshift connector allows querying the data stored in an external Amazon Redshift cluster. This can be used to join data between different systems like Redshift and Hive, or between two different Redshift clusters.
This guide was developed using a laptop running Windows OS and docker on it.
This guide was developed using a laptop running Windows OS and docker on it.
Apache Calcite is a dynamic data management framework with SQL parser, optimizer, executor, and JDBC driver.
Many examples of Apache Calcite usage demonstrate the end-to-end execution of queries using JDBC driver, some built-in optimization rules, and the Enumerable executor. Our customers often have their own execution engines and JDBC drivers. So how to use Apache Calcite for query optimization only, without its JDBC driver and Enumerable executor?
We often forget that each request is not independent of other requests. If one request is slow, it’s unlikely to affect the others… right?
The database is a shared resource used by all processes that runs in your application. Even just one poorly designed access can hurt the performance of the whole system.
FROM customer USE KEYS ["cx:123"] Couchbase is a distributed database. It supports a flexible data model using JSON. Each document in a bucket will have a user-generated unique document key. This uniqueness is enforced during the insertion or updation of the data. Here’s an example document.
select meta().id, travel
from `travel-sample` travel
where type = 'airline' limit 1;
[
{
"id": "airline_10",
"travel": {
"callsign": "MILE-AIR",
"country": "United States",
"iata": "Q5",
"icao": "MLA",
"id": 10,
"name": "40-Mile Air",
"type": "airline"
}
}
]
INSERT INTO customer (key, value) VALUES(“cx:123”, {“name”:”joe”, “zip”: 94040, “state”:ca});
SELECT META().id FROM customer;
cx:123