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Neo4j vs Presto: What are the differences?
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Data Model: Neo4j is a graph database that stores data in nodes connected by relationships, allowing for complex relationships to be easily represented. On the other hand, Presto is a distributed SQL query engine for big data that does not have a native graph data model, making it more suitable for traditional relational and columnar data.
Query Language: Neo4j uses Cypher, a graph query language designed specifically for graph databases, allowing users to write expressive queries for traversing the graph data. In contrast, Presto uses SQL as its query language, enabling users familiar with SQL to work with diverse data sources efficiently.
Scalability: Neo4j is a NoSQL database that provides low latency and high availability by distributing data across multiple servers, but it may be limited in scalability for massive datasets. Presto, being a distributed query engine, can scale horizontally to handle large volumes of data through parallel processing across a cluster of machines.
Use Case: Neo4j is ideal for applications that require intricate relationship mapping, such as social networks, recommendations, and fraud detection, where relationships are crucial in the data model. Presto, on the other hand, is suitable for analytical queries on large datasets where real-time analysis and interactive querying are needed without the complexity of graph traversal.
Architecture: Neo4j is designed as a native graph database, where data is stored in the form of nodes and relationships, optimized for graph processing. In contrast, Presto follows a shared-nothing architecture, where each node communicates independently to serve queries, enabling better scalability and resource utilization for distributed querying.
Community Support: Neo4j has a strong community focused on graph database applications, providing extensive resources, plugins, and support for developers working with graph data. Presto, originally developed by Facebook and now under the Presto Software Foundation, has a growing community specializing in distributed data processing and analytics, ensuring continued development and enhancement of the query engine.
In Summary, Neo4j focuses on graph processing and complex relationships with Cypher as its query language, while Presto is geared towards distributed querying of large datasets using SQL. Both have distinct strengths and are suited for different use cases.
To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.
Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.
We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.
Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.
Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.
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The platform deals with time series data from sensors aggregated against things( event data that originates at periodic intervals). We use Cassandra as our distributed database to store time series data. Aggregated data insights from Cassandra is delivered as web API for consumption from other applications. Presto as a distributed sql querying engine, can provide a faster execution time provided the queries are tuned for proper distribution across the cluster. Another objective that we had was to combine Cassandra table data with other business data from RDBMS or other big data systems where presto through its connector architecture would have opened up a whole lot of options for us.
Pros of Neo4j
- Cypher – graph query language69
- Great graphdb61
- Open source33
- Rest api31
- High-Performance Native API27
- ACID23
- Easy setup21
- Great support17
- Clustering11
- Hot Backups9
- Great Web Admin UI8
- Powerful, flexible data model7
- Mature7
- Embeddable6
- Easy to Use and Model5
- Highly-available4
- Best Graphdb4
- It's awesome, I wanted to try it2
- Great onboarding process2
- Great query language and built in data browser2
- Used by Crunchbase2
Pros of Presto
- Works directly on files in s3 (no ETL)18
- Open-source13
- Join multiple databases12
- Scalable10
- Gets ready in minutes7
- MPP6
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Cons of Neo4j
- Comparably slow9
- Can't store a vertex as JSON4
- Doesn't have a managed cloud service at low cost1
Cons of Presto
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