Getting started for developers


The main questions that are being answered here are: How to fetch data? and What to do with the data?.

There are a few different ways to fetch and analyze data data:


If the desired metric is available in the API using it is the preferred way to fetch data. Detailed description and examples can be found on the SanAPI page

The API can be consumed in a few different ways:

  • Use the GraphiQL Live Explorer and explore the API with included autocmplete and nice response formatting.
  • Use the /graphql API endpoint with curl directly from your terminal.
  • Use the /graphql API endpoint and construct requests in your preferred programming language. There are examples in R, Ruby, Javascript and Elixir
  • Use the sanpy Python library that wraps the GraphQL API. It is easy to use and hides all GraphQL-related details.

GraphQL API Complexity

The metric API queries like this NVT example can potentially produce huge results (hundreds of thousands or millions of data points) or produce queries that scan the whole database and spend too many resources. Allowing such behavior opens doors to DoS attacks.

In order to guard against such behavior, every GraphQL request is analyzed before being executed. The complexity for every query is computed, and if it goes above the threshold, the API server rejects it and returns an error. Roughly speaking, the complexity is proportional to the number of data points returned.

More accurately, the complexity computation takes into consideration the following:

  • Number of data points returned - N. Fetching 30 days of data at daily intervals results in 30 data points.
  • Number of fields in a data point - F. For most metrics, this includes two fields: datetime and value.
  • Metric weight - W. Most of the metrics are stored in specialized fast data storage, so they have a smaller weight (0.3). The rest of the metrics have a weight of 1.
  • Years time range span - Y. If the request is using a big interval (like 30 days) the number of data points is small. However, the time range spans several years. The query still needs to read and aggregate a lot of data in the database.
  • Subscription plan tier - S. The higher the user's plan, the bigger the complexity limit is (3 for Basic, 5 for Pro, 7 for Premium). As the complexity threshold is constant, the computed complexity is divided by S. This has the effect that the same query executed by a Pro user will have a 5 times smaller complexity than the same query executed by a Free user.

With the above-defined values, the complexity is computed by the following formula: $$ Complexity(Q) := \dfrac{N(Q) F(Q) W(Q) * Y(Q)}{S(Q)} $$ where Q is the query that is being analyzed, and N(Q)...S(Q) are the described values computed on that query.


Let us see how to compute the complexity when a SanAPI PRO subscription user executes the following query.

  getMetric(metric: "price_usd"){
    timeseriesData(slug: "bitcoin" from: "utc_now-3650d" to: "utc_now" interval: "1h"){

N(Q) = 3750 * 24 = 90000 - The time range is 3650 days and the interval is 1 hour. F(Q) = 2 - Every data point contains two fields - datetime and value. W(Q) = 0.3 Y(Q) = 4 (Computed as: max(2022-2012, 2) / 2) S(Q) = 5

$$ Complexity(Q) := \dfrac{90000 2 0.3 * 4}{5} = 43200 $$

The complexity threshold is 50000, so this query passes the analysis, and the API server executes it. If a SanAPI Free user executes this query, S(Q) = 1 and the complexity will be over 210000. This will result in the following error:

Operation is too complex: complexity is 210241 and maximum is 50000

Download CSV from Sanbase

Sanbase contains only data available in the API. Data from charts can be exported as a CSV file. sanbase-csv-export

Download CSV from API no-code way

API has only json output format, but you can ease to use this sample to get csv file. CSV API Download Tool

Download CSV from Sangraphs

Sangraphs contains metrics that are both available and not available in the API. The social merics from Sangraphs can be exported as a CSV file from the bottom of the social page. sangraphs-csv-export

Analyzing Santiment Data

Examples for different analysis based on Santiment data can be found on the Education and use cases page

The are two types of examples included:

  • Jupyter Notebooks where code is written to analyze the data and plot results
  • Descriptions how to interpert the chart data available in Santiment products

Was this article helpful?