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Version: v4.13


A Tenzir pipeline is a chain of operators that represents a dataflow. Operators are the atomic building blocks that produce, transform, or consume data. Think of them as UNIX or Powershell commands where output from one command is input to the next:

There exist three types of operators: sources that produce data, sinks that consume data, and transformations that do both:

Typed Operators

Tenzir pipelines make one more distinction: the elements that the operators push through the pipeline are typed. Every operator has an input and an output type:

When composing pipelines out of operators, the type of adjacent operators have to match. Otherwise the pipeline is malformed. Here's an example pipeline with matching operators:

We call any void-to-void operator sequence a closed pipeline. Only closed pipelines can execute. If a pipeline does not have a source and sink, it would "leak" data. If a pipeline is open, the engine auto-completes a source/sink when possible and rejects the pipeline otherwise. Auto-completion is context dependent: on the command line we read JSON from stdin and write it stdout. In the app we only auto-complete a missing sink with serve to display the result in the browser.

Because pipelines can transport the two types bytes and events, we can cleanly model the data lifecycle as separate stages:

Data acquisition typically begins with a source operator that uses a loader, such as load, to acquire bytes as a side effect. Then an operator, such as read, transforms bytes into events with the help of a parser. This effectively turns unstructured data into structured data. There exist numerous operators that bring structured data into the desired form for a specific use case—a process we call shaping. After shaping yielded the desired form, the data leaves the pipeline in reverse order. A printer creates bytes from events, e.g., using write. Finally, a sink operator, such as save, uses a saver to write the rendered bytes into a specific location.

Multi-Schema Dataflows

Tenzir dataflows are multi-schema in that a single pipeline can work with heterogeneous types of events, each of which have a different schemas. This allows you, for example, to perform aggregations across multiple events. Multi-schema dataflows require automatic schema inference at parse time. Tenzir parsers, such as json support this out of the box. This behavior is different from engines that work with structured data where operators typically work with fixed set of tables. While Schema-less systems, such as document-oriented databases, offer more simplicity, their one-record-at-a-time processing comes at the cost of performance.

If the schema in a pipeline changes, we simply create a new batch of events. The worst case for Tenzir is a ordered stream of schema-switching events, with every event having a new schema than the previous one. That said, even for those data streams we can efficiently build homogeneous batches when the inter-event order does not matter significantly. Similar to predicate pushdown, Tenzir operators support "ordering pushdown" to signal to upstream operators that the event order only matters intra-schema but not inter-schema. In this case we transparently demultiplex a heterogeneous stream into N homogeneous streams, each of which yields batches of up to 65k events. The import operator is an example of such an operator, and it pushes its ordering upstream so that we can efficiently parse, say, a diverse stream of NDJSON records, such as Suricata's EVE JSON or Zeek's streaming JSON.

You could call multi-schema dataflows multiplexed and there exist dedicated operators to demultiplex a stream. As of now, this is hard-coded per operator. For example, to directory /tmp/dir write parquet demultiplexes a stream of events so that batches with the same schema go to the same Parquet file.

The diagram below illustrates the multi-schema aspect of dataflows for schemas A, B, and C:

Some operators only work with exactly one instance per schema internally, such as write when combined with the parquet, feather, or csv formats. These formats cannot handle multiple input schemas at once. A demultiplexing operator like to directory .. write <format> removes this limitation by writing one file per schema instead.

We are having ideas to make this schema (de)multiplexing explicit with a per-schema operator modifier that you can write in front of every operator. Similarly, we are going to add union types in the future, making it possible to convert a heterogeneous stream of structured data into a homogeneous one.

It's important to note that most of the time you don't have to worry about schemas. They are there for you when you want to work with them, but it's often enough to just specified the fields that you want to work with, e.g., where id.orig_h in, or select src_ip, dest_ip, proto. Schemas are inferred automatically in parsers, but you can also seed a parser with a schema that you define explicitly.

Unified Live Stream Processing and Historical Queries

Systems for stream processing and running historical queries have different requirements, and combining them into a single system can be a daunting challenge. But there is an architectural sweetspot at the right level of abstraction where you can elegantly combine them. From a user persepctive, our goal was to seamlessly exchange the beginning of a pipeline to select the source of the data, be it a historical or continuous one:

Our desired user experience for interacting with historical looks like this:

  1. Ingest: to persist data at a node, create a pipeline that ends with the import sink.
  2. Query: to run a historical query, create a pipeline that begins with the export operator.

For example, to ingest JSON from a Kafka, you write from kafka --topic foo | import. To query the stored data, you write export | where file == 42. The latter example suggests that the pipeline first exports everything, and only then starts filtering with where, performing a full scan over the stored data. But this is not what's happening. Our pipelines support predicate pushdown for every operator. This means that export receives the filter expression before it starts executing, enabling index lookups or other optimizations to efficiently execute queries with high selectivity where scans would be sub-optimal.

The central insight here is to ensure that predicate pushdown (as well as other forms of signalling) exist throughout the entire pipeline engine, and that the engine can communicate this context to the storage engine.

Our own storage engine is not a full-fledged database, but rather a thin indexing layer over a set of Parquet/Feather files. The sparse indexes (sketch data structures, such as min-max synopses, Bloom filters, etc.) avoid full scans for every query. The storage engine also has a catalog that tracks evolving schemas, performs expression binding, and provides a transactional interface to add and replace partitions during compaction.

The diagram below shows the main components of the database engine:

Because of this transparent optimization, you can just exchange the source of a pipeline and switch between historical and streaming execution without degrading performance. A typical use case begins some exploratory data analysis involving a few export pipelines, but then would deploy the pipeline on streaming data by exchanging the source with, say, from kafka.

The difference between import and from file <path> read parquet (or export and to file <path> write parquet) is that the storage engine has the extra catalog and indexes, managing the complexity of dealing with a large set of raw Parquet files.

Built-in Networking to Create Data Fabrics

Tenzir pipelines have built-in network communication, allowing you to create a distributed fabric of dataflows to express intricate use cases. There are two types of network connections: implicit and explicit ones:

An implicit network connection exists, for example, when you use the tenzir binary on the command line to run a pipeline that ends in import:

tenzir 'load gcs bkt/eve.json
| read suricata
| where #schema != "suricata.stats"
| import

This results in the following pipeline execution:

A historical query, like export | where <expr> | to <connector>, has the network connection at the other end:

Tenzir pipelines are eschewing networking to minimize latency and maximize throughput. So we generally transfer ownership of operators between processes as late as possible to prefer local, high-bandwidth communication. For maximum control over placement of computation, you can override the automatic operator location with the local and remote operator modifiers.

The above examples are implicit network connections because they're not visible in the pipeline definition. An explicit network connection terminates a pipeline as source or sink:

This fictive data fabric above consists of a heterogeneous set of technologies, interconnected by pipelines. You can also turn any pipeline into an API using the serve sink, effectively creating a dataflow microservice that you can access with a HTTP client from the other side:

Because you have full control over the location where you run the pipeline, you can push it all the way to the "last mile." This helps especially when there are compliance and data residency concerns that must be properly addressed.