Google Cloud Spanner vs InfluxDB

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Google Cloud Spanner

34
94
+ 1
3
InfluxDB

955
1.1K
+ 1
169
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InfluxDB vs Google Cloud Spanner: What are the differences?

Developers describe InfluxDB as "An open-source distributed time series database with no external dependencies". InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.. On the other hand, Google Cloud Spanner is detailed as "Fully managed, scalable, relational database service for regional and global application data". It is a globally distributed database service that gives developers a production-ready storage solution. It provides key features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover.

InfluxDB and Google Cloud Spanner belong to "Databases" category of the tech stack.

Some of the features offered by InfluxDB are:

  • Time-Centric Functions
  • Scalable Metrics
  • Events

On the other hand, Google Cloud Spanner provides the following key features:

  • Global transactions
  • Strongly consistent reads
  • Automatic multi-site replication

InfluxDB is an open source tool with 17.5K GitHub stars and 2.47K GitHub forks. Here's a link to InfluxDB's open source repository on GitHub.

Advice on Google Cloud Spanner and InfluxDB
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InfluxDBInfluxDBMongoDBMongoDB
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TimescaleDBTimescaleDB

We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.

So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily

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Replies (3)
Yaron Lavi
Recommends
PostgreSQLPostgreSQL

We had a similar challenge. We started with DynamoDB, Timescale, and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us a We had a similar challenge. We started with DynamoDB, Timescale and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us better performance by far.

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Recommends
DruidDruid

Druid is amazing for this use case and is a cloud-native solution that can be deployed on any cloud infrastructure or on Kubernetes. - Easy to scale horizontally - Column Oriented Database - SQL to query data - Streaming and Batch Ingestion - Native search indexes It has feature to work as TimeSeriesDB, Datawarehouse, and has Time-optimized partitioning.

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Ankit Malik
Software Developer at CloudCover · | 3 upvotes · 187.7K views
Recommends
Google BigQueryGoogle BigQuery

if you want to find a serverless solution with capability of a lot of storage and SQL kind of capability then google bigquery is the best solution for that.

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Decisions about Google Cloud Spanner and InfluxDB
Benoit Larroque
Principal Engineer at Sqreen · | 2 upvotes · 89.7K views

I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.

The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)

We are combining this with Grafana for display and Telegraf for data collection

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Pros of Google Cloud Spanner
Pros of InfluxDB
  • 1
    Strongly consistent
  • 1
    Horizontal scaling
  • 1
    Scalable
  • 54
    Time-series data analysis
  • 30
    Easy setup, no dependencies
  • 24
    Fast, scalable & open source
  • 21
    Open source
  • 19
    Real-time analytics
  • 6
    Continuous Query support
  • 5
    Easy Query Language
  • 4
    HTTP API
  • 4
    Out-of-the-box, automatic Retention Policy
  • 1
    Offers Enterprise version
  • 1
    Free Open Source version

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Cons of Google Cloud Spanner
Cons of InfluxDB
    Be the first to leave a con
    • 4
      Instability
    • 1
      HA or Clustering is only in paid version

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    - No public GitHub repository available -

    What is Google Cloud Spanner?

    It is a globally distributed database service that gives developers a production-ready storage solution. It provides key features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover.

    What is InfluxDB?

    InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

    Need advice about which tool to choose?Ask the StackShare community!

    Jobs that mention Google Cloud Spanner and InfluxDB as a desired skillset
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    What companies use Google Cloud Spanner?
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    See which teams inside your own company are using Google Cloud Spanner or InfluxDB.
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    What tools integrate with Google Cloud Spanner?
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    What are some alternatives to Google Cloud Spanner and InfluxDB?
    Google Cloud SQL
    Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.
    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.
    Oracle
    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
    Google Cloud Datastore
    Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.
    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.
    See all alternatives