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ArangoDB vs Apache Spark: What are the differences?

ArangoDB: A distributed open-source database with a flexible data model for documents, graphs, and key-values. A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions; Apache Spark: Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

ArangoDB and Apache Spark are primarily classified as "Databases" and "Big Data" tools respectively.

Some of the features offered by ArangoDB are:

  • multi-model nosql db
  • acid
  • transactions

On the other hand, Apache Spark provides the following key features:

  • Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
  • Write applications quickly in Java, Scala or Python
  • Combine SQL, streaming, and complex analytics

"Grahps and documents in one DB" is the top reason why over 24 developers like ArangoDB, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.

ArangoDB and Apache Spark are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than ArangoDB with 8.22K GitHub stars and 576 GitHub forks.

Uber Technologies, Slack, and Shopify are some of the popular companies that use Apache Spark, whereas ArangoDB is used by AresRPG, Stepsize, and Brainhub. Apache Spark has a broader approval, being mentioned in 266 company stacks & 112 developers stacks; compared to ArangoDB, which is listed in 11 company stacks and 15 developer stacks.

What is ArangoDB?

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

What is Apache Spark?

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
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What are some alternatives to ArangoDB and Apache Spark?
Neo4j
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
PostgreSQL
PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
OrientDB
It is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
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Decisions about ArangoDB and Apache Spark
StackShare Editors
StackShare Editors
Presto
Presto
Apache Spark
Apache Spark
Hadoop
Hadoop

Around 2015, the growing use of Uber’s data exposed limitations in the ETL and Vertica-centric setup, not to mention the increasing costs. “As our company grew, scaling our data warehouse became increasingly expensive. To cut down on costs, we started deleting older, obsolete data to free up space for new data.”

To overcome these challenges, Uber rebuilt their big data platform around Hadoop. “More specifically, we introduced a Hadoop data lake where all raw data was ingested from different online data stores only once and with no transformation during ingestion.”

“In order for users to access data in Hadoop, we introduced Presto to enable interactive ad hoc user queries, Apache Spark to facilitate programmatic access to raw data (in both SQL and non-SQL formats), and Apache Hive to serve as the workhorse for extremely large queries.

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StackShare Editors
StackShare Editors
Presto
Presto
Apache Spark
Apache Spark
Hadoop
Hadoop

To improve platform scalability and efficiency, Uber transitioned from JSON to Parquet, and built a central schema service to manage schemas and integrate different client libraries.

While the first generation big data platform was vulnerable to upstream data format changes, “ad hoc data ingestions jobs were replaced with a standard platform to transfer all source data in its original, nested format into the Hadoop data lake.”

These platform changes enabled the scaling challenges Uber was facing around that time: “On a daily basis, there were tens of terabytes of new data added to our data lake, and our Big Data platform grew to over 10,000 vcores with over 100,000 running batch jobs on any given day.”

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StackShare Editors
StackShare Editors
Presto
Presto
Apache Spark
Apache Spark
Scala
Scala
MySQL
MySQL
Kafka
Kafka

Slack’s data team works to “provide an ecosystem to help people in the company quickly and easily answer questions about usage, so they can make better and data informed decisions.” To achieve that goal, that rely on a complex data pipeline.

An in-house tool call Sqooper scrapes MySQL backups and pipe them to S3. Job queue and log data is sent to Kafka then persisted to S3 using an open source tool called Secor, which was created by Pinterest.

For compute, Amazon’s Elastic MapReduce (EMR) creates clusters preconfigured for Presto, Hive, and Spark.

Presto is then used for ad-hoc questions, validating data assumptions, exploring smaller datasets, and creating visualizations for some internal tools. Hive is used for larger data sets or longer time series data, and Spark allows teams to write efficient and robust batch and aggregation jobs. Most of the Spark pipeline is written in Scala.

Thrift binds all of these engines together with a typed schema and structured data.

Finally, the Hive Metastore serves as the ground truth for all data and its schema.

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StackShare Editors
StackShare Editors
Apache Thrift
Apache Thrift
Kotlin
Kotlin
Presto
Presto
HHVM (HipHop Virtual Machine)
HHVM (HipHop Virtual Machine)
gRPC
gRPC
Kubernetes
Kubernetes
Apache Spark
Apache Spark
Airflow
Airflow
Terraform
Terraform
Hadoop
Hadoop
Swift
Swift
Hack
Hack
Memcached
Memcached
Consul
Consul
Chef
Chef
Prometheus
Prometheus

Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

Apps
  • Web: a mix of JavaScript/ES6 and React.
  • Desktop: And Electron to ship it as a desktop application.
  • Android: a mix of Java and Kotlin.
  • iOS: written in a mix of Objective C and Swift.
Backend
  • The core application and the API written in PHP/Hack that runs on HHVM.
  • The data is stored in MySQL using Vitess.
  • Caching is done using Memcached and MCRouter.
  • The search service takes help from SolrCloud, with various Java services.
  • The messaging system uses WebSockets with many services in Java and Go.
  • Load balancing is done using HAproxy with Consul for configuration.
  • Most services talk to each other over gRPC,
  • Some Thrift and JSON-over-HTTP
  • Voice and video calling service was built in Elixir.
Data warehouse
  • Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
Etc
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Eric Colson
Eric Colson
Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 207.5K views
atStitch FixStitch Fix
Amazon EC2 Container Service
Amazon EC2 Container Service
Docker
Docker
PyTorch
PyTorch
R
R
Python
Python
Presto
Presto
Apache Spark
Apache Spark
Amazon S3
Amazon S3
PostgreSQL
PostgreSQL
Kafka
Kafka
#Data
#DataStack
#DataScience
#ML
#Etl
#AWS

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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Interest over time
Reviews of ArangoDB and Apache Spark
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How developers use ArangoDB and Apache Spark
Avatar of Wei Chen
Wei Chen uses Apache SparkApache Spark

Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.

Avatar of King's Digital Lab
King's Digital Lab uses ArangoDBArangoDB

Multi-model DB: document (JSON), graph, key-value

TO BE EVALUATED

Avatar of Ralic Lo
Ralic Lo uses Apache SparkApache Spark

Used Spark Dataframe API on Spark-R for big data analysis.

Avatar of BrainFinance
BrainFinance uses Apache SparkApache Spark

As a part of big data machine learning stack (SMACK).

Avatar of Kalibrr
Kalibrr uses Apache SparkApache Spark

We use Apache Spark in computing our recommendations.

Avatar of Dotmetrics
Dotmetrics uses Apache SparkApache Spark

Big data analytics and nightly transformation jobs.

Avatar of Stefan Gojan
Stefan Gojan uses ArangoDBArangoDB

Persist user actions and information in documents

Avatar of Minyoung Kim
Minyoung Kim uses ArangoDBArangoDB

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