ObjectBox Java (Kotlin, Android) VS Apache Hive

When I first attempted to publish to F-Droid, I experienced several pipeline issues. After reading through the pipeline logs in GitLab, I realized that my application's database (ObjectBox) was not entirely FOSS compliant and was causing build failures. The following day was spent migrating my app to Room.

I would focus on Kotlin instead of Java, there's really no point in sticking to Java at this point. And when it comes to databases, some local ones that are pretty easy to get into are Realm and ObjectBox, SQLite can definitely be a bit overwhelming at the beginning.

Just to add to this, there's also Realm and ObjectBox as alternatives.

Adding ObjectBox [0] to the list.

[0] https://objectbox.io/

Obectbox

Has anyone used ObjectBox in anger and can share their experience?

https://objectbox.io/

https://github.com/objectbox/

ObjectBox

There are a lot of persistent database solutions available for Flutter applications. One of them is ObjectBox which is a NoSQL-based high-performance-oriented database solution written in the native dart language. You can use local as well as server features for syncing data as you like.

Trino or Hive for SQL querying. Get Trino/Hive to talk to Nessie.

Hive, A data warehouse infrastructure that provides data summarization and ad hoc querying.

Apache Drill, Druid, Flink, Hive, Kafka, Spark

In this article, I'm showing you how to create a Spring Boot app that loads data from Apache Hive via Apache Spark to the Aerospike Database. More than that, I'm giving you a recipe for writing integration tests for such scenarios that can be run either locally or during the CI pipeline execution. The code examples are taken from this repository.

ListItem(name='Apache Hive', website='https://hive.apache.org/', category='Interactive Query', short_description='Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.'),

Apache Hive takes in a specific SQL dialect and converts it to map-reduce.

Apache Hive

Apache Calcite is a popular parser/optimizer that is used in popular databases and query engines like Apache Hive, BlazingSQL and many others.

> whether this would really work out in most workloads

> just because it keeps the cache-lines hotter and less likely to be evicted.

Okay, so keeping cache for a bloom filter problem is real - but the real force evicting memory out of the cache line is the next row-group you read + all the other stuff you have to do when you implement this in a database product.

So the two things I work with, Apache Hive and Apache Impala switched to a blocked bloom filter at different points in time.

Hive BloomKFilter - https://github.com/apache/hive/blob/master/storage-api/src/j...

Impala/Kudu one - https://github.com/apache/impala/blob/master/be/src/kudu/uti...

The C++ one also has an AVX specialization, while the Java one relies on the JVM to do it (not always) - https://github.com/apache/impala/blob/master/be/src/kudu/uti...

We ran a lot of trivial benchmarks and several benchmarks where the shuffle-join (not sort-merge, this is just a partitioned hash join) generates a bloom filter (a semijoin) before sending rows out and the 1-cache line version won out when the bloom filter went slightly over the 1 Million + 5% rate [1].

The regular bloom filter went from (38ns -> 108ns for 1k -> 1m items), while the BloomK stuck at (27ns) despite making room for a million times more items in the bloom. The bloom-1 (which is the 64bit version) underperformed on accuracy (was ~2x faster at 16ns per op, but worse at filtering out items).

[1] - https://github.com/prasanthj/bloomfilter/tree/master/benchma...