Data processing in a MediaPipe graph occurs inside processing nodes defined as
CalculatorBase subclasses. The scheduling system decides when each
calculator should run.
Each graph has at least one scheduler queue. Each scheduler queue has exactly one executor. Nodes are statically assigned to a queue (and therefore to an executor). By default there is one queue, whose executor is a thread pool with a number of threads based on the system’s capabilities.
Each node has a scheduling state, which can be not ready, ready, or running. A readiness function determines whether a node is ready to run. This function is invoked at graph initialization, whenever a node finishes running, and whenever the state of a node’s inputs changes.
The readiness function used depends on the type of node. A node with no stream inputs is known as a source node; source nodes are always ready to run, until they tell the framework they have no more data to output, at which point they are closed.
Non-source nodes are ready if they have inputs to process, and if those inputs form a valid input set according to the conditions set by the node’s input policy (discussed below). Most nodes use the default input policy, but some nodes specify a different one.
When a node becomes ready, a task is added to the corresponding scheduler queue, which is a priority queue. The priority function is currently fixed, and takes into account static properties of the nodes and their topological sorting within the graph. For example, nodes closer to the output side of the graph have higher priority, while source nodes have the lowest priority.
Each queue is served by an executor, which is responsible for actually running the task by invoking the calculator’s code. Different executors can be provided and configured; this can be used to customize the use of execution resources, e.g. by running certain nodes on lower-priority threads.
MediaPipe graph execution is decentralized: there is no global clock, and different nodes can process data from different timestamps at the same time. This allows higher throughput via pipelining.
However, time information is very important for many perception workflows. Nodes that receive multiple input streams generally need to coordinate them in some way. For example, an object detector may output a list of boundary rectangles from a frame, and this information may be fed into a rendering node, which should process it together with the original frame.
Therefore, one of the key responsibilities of the MediaPipe framework is to provide input synchronization for nodes. In terms of framework mechanics, the primary role of a timestamp is to serve as a synchronization key.
Furthermore, MediaPipe is designed to support deterministic operations, which is important in many scenarios (testing, simulation, batch processing, etc.), while allowing graph authors to relax determinism where needed to meet real-time constraints.
The two objectives of synchronization and determinism underlie several design
choices. Notably, the packets pushed into a given stream must have monotonically
increasing timestamps: this is not just a useful assumption for many nodes, but
it is also relied upon by the synchronization logic. Each stream has a
timestamp bound, which is the lowest possible timestamp allowed for a new
packet on the stream. When a packet with timestamp
T arrives, the bound
automatically advances to
T+1, reflecting the monotonic requirement. This
allows the framework to know for certain that no more packets with timestamp
T will arrive.
Synchronization is handled locally on each node, using the input policy specified by the node.
The default input policy, defined by
deterministic synchronization of inputs, with the following guarantees:
If packets with the same timestamp are provided on multiple input streams, they will always be processed together regardless of their arrival order in real time.
Input sets are processed in strictly ascending timestamp order.
No packets are dropped, and the processing is fully deterministic.
The node becomes ready to process data as soon as possible given the guarantees above.
To explain how it works, we need to introduce the definition of a settled timestamp. We say that a timestamp in a stream is settled if it is lower than the timestamp bound. In other words, a timestamp is settled for a stream once the state of the input at that timestamp is irrevocably known: either there is a packet, or there is the certainty that a packet with that timestamp will not arrive.
A timestamp is settled across multiple streams if it is settled on each of those streams. Furthermore, if a timestamp is settled it implies that all previous timestamps are also settled. Thus settled timestamps can be processed deterministically in ascending order.
Given this definition, a calculator with the default input policy is ready if there is a timestamp which is settled across all input streams and contains a packet on at least one input stream. The input policy provides all available packets for a settled timestamp as a single input set to the calculator.
One consequence of this deterministic behavior is that, for nodes with multiple input streams, there can be a theoretically unbounded wait for a timestamp to be settled, and an unbounded number of packets can be buffered in the meantime. (Consider a node with two input streams, one of which keeps sending packets while the other sends nothing and does not advance the bound.)
Therefore, we also provide for custom input policies: for example, splitting the
inputs in different synchronization sets defined by
SyncSetInputStreamHandler, or avoiding synchronization altogether and
processing inputs immediately as they arrive defined by
There are two main flow control mechanisms. A backpressure mechanism throttles
the execution of upstream nodes when the packets buffered on a stream reach a
(configurable) limit defined by
mechanism maintains deterministic behavior, and includes a deadlock avoidance
system that relaxes configured limits when needed.
The second system consists of inserting special nodes which can drop packets
according to real-time constraints (typically using custom input policies)
FlowLimiterCalculator. For example, a common pattern places a
flow-control node at the input of a subgraph, with a loopback connection from
the final output to the flow-control node. The flow-control node is thus able to
keep track of how many timestamps are being processed in the downstream graph,
and drop packets if this count hits a (configurable) limit; and since packets
are dropped upstream, we avoid the wasted work that would result from partially
processing a timestamp and then dropping packets between intermediate stages.
This calculator-based approach gives the graph author control of where packets can be dropped, and allows flexibility in adapting and customizing the graph’s behavior depending on resource constraints.