Skip to main content
Version: v1.1.0


TL;DR zed is a command-line tool to manage and query Zed data lakes. You can import data from a variety of formats and zed will automatically commit the data in the Zed data model's super-structured format, providing full fidelity of the original format and the ability to reconstruct the original data without loss of information.

Zed lakes provide an easy-to-use substrate for data discovery, preparation, and transformation as well as serving as a queryable and searchable store for super-structured data both for online and archive use cases.


While zq and the Zed formats are production quality, the Zed lake is still fairly early in development and alpha quality. That said, Zed lakes can be utilized quite effectively at small scale, or at larger scales when scripted automation is deployed to manage the lake's data layout and create any needed search indexes via the lake API.

Enhanced scalability with self-tuning configuration is under development.

1. The Lake Model

A Zed lake is a cloud-native arrangement of data, optimized for search, analytics, ETL, data discovery, and data preparation at scale based on data represented in accordance with the Zed data model.

A lake is organized into a collection of data pools forming a single administrative domain. The current implementation supports ACID append and delete semantics at the commit level while we have plans to support CRUD updates at the primary-key level in the near future.

The semantics of a Zed lake loosely follows the nomenclature and design patterns of git. In this approach,

  • a lake is like a GitHub organization,
  • a pool is like a git repository,
  • a branch of a pool is like a git branch,
  • the use command is like a git checkout, and
  • the load command is like a git add/commit/push.

A core theme of the Zed lake design is ergonomics. Given the Git metaphor, our goal here is that the Zed lake tooling be as easy and familiar as Git is to a technical user.

Since Zed lakes are built around the Zed data model, getting different kinds of data into and out of a lake is easy. There is no need to define schemas or tables and then fit semi-structured data into schemas before loading data into a lake. And because Zed supports a large family of formats and the load endpoint automatically detects most formats, it's easy to just load data into a lake without thinking about how to convert it into the right format.

1.1 CLI-First Approach

The Zed project has taken a CLI-first approach to designing and implementing the system. Any time a new piece of functionality is added to the lake, it is first implemented as a zed command. This is particularly convenient for testing and continuous integration as well as providing intuitive, bite-sized chunks for learning how the system works and how the different components come together.

While the CLI-first approach provides these benefits, all of the functionality is also exposed through an API to a Zed service. Many use cases involve an application like Brim or a programming environment like Python/Pandas interacting with the service API in place of direct use with the zed command.

1.2 Storage Layer

The Zed lake storage model is designed to leverage modern cloud object stores and separates compute from storage.

A lake is entirely defined by a collection of cloud objects stored at a configured object-key prefix. This prefix is called the storage path. All of the meta-data describing the data pools, branches, commit history, and so forth is stored as cloud objects inside of the lake. There is no need to set up and manage an auxiliary metadata store.

Data is arranged in a lake as a set of pools, which are comprised of one or more branches, which consist of a sequence of data commit objects that point to cloud data objects.

Cloud objects and commits are immutable and named with globally unique IDs, based on the KSUIDs, and many commands may reference various lake entities by their ID, e.g.,

  • Pool ID - the KSUID of a pool
  • Commit object ID - the KSUID of a commit object
  • Data object ID - the KSUID of a committed data object
  • Index rule ID - the KSUID of an index rule
  • Index object ID - the KSUID of an index object relative to a data object

Data is added and deleted from the lake only with new commits that are implemented in a transactionally consistent fashion. Thus, each commit object (identified by its globally-unique ID) provides a completely consistent view of an arbitrarily large amount of committed data at a specific point in time.

While this commit model may sound heavyweight, excellent live ingest performance can be achieved by micro-batching commits.

Because the Zed lake represents all state transitions with immutable objects, the caching of any cloud object (or byte ranges of cloud objects) is easy and effective since a cached object is never invalid. This design makes backup/restore, data migration, archive, and replication easy to support and deploy.

The cloud objects that comprise a lake, e.g., data objects, commit history, transaction journals, search indexes, partial aggregations, etc., are stored as Zed data, i.e., either as row-based ZNG or columnar ZST. This makes introspection of the lake structure straightforward as many key lake data structures can be queried with metadata queries and presented to a client as Zed data for further processing by downstream tooling.

Zed's implementation also includes a storage abstraction that maps the cloud object model onto a file system so that Zed lakes can also be deployed on standard file systems.

1.3 Zed Command Personalities

The zed command provides a single command-line interface to Zed lakes, but different personalities are taken on by zed depending on the particular sub-command executed and the disposition of its -lake option (which defaults to the value of ZED_LAKE environment variable or, if ZED_LAKE is not set, to the client personality https://localhost:9867).

To this end, zed can take on one of three personalities:

  • Direct Access - When the lake is a storage path (file or s3 URI), then the zed commands (except for serve) all operate directly on the lake located at that path.
  • Client Personality - When the lake is an HTTP or HTTPS URL, then the lake is presumed to be a Zed lake service endpoint and the client commands are directed to the service managing the lake.
  • Server Personality - When the zed serve command is executed, then the personality is always the server personality and the lake must be a storage path. This command initiates a continuous server process that serves client requests for the lake at the configured storage path.

Note that a storage path on the file system may be specified either as a fully qualified file URI of the form file:// or be a standard file system path, relative or absolute, e.g., /lakes/test.

Concurrent access to any Zed lake storage, of course, preserves data consistency. You can run multiple zed serve processes while also running any zed lake command all pointing at the same storage endpoint and the lake's data footprint will always remain consistent as the endpoints all adhere to the consistency semantics of the Zed lake.

One caveat here: data consistency is not fully implemented yet for the S3 endpoint so only single-node access to S3 is available right now, though support for multi-node access is forthcoming. For a shared file system, the close-to-open cache consistency semantics of NFS should provide the necessary consistency guarantees needed by a Zed lake though this has not been tested. Multi-process, single-node access to a local file system has been thoroughly tested and should be deemed reliable, i.e., you can run a direct-access instance of zed alongside a server instance of zed on the same file system and data consistency will be maintained.

1.4 Data Pools

A lake is made up of data pools, which are like "collections" in NoSQL document stores. Pools may have one or more branches and every pool always has a branch called main.

A pool is created with the create command and a branch of a pool is created with the branch command.

A pool name can be any valid UTF-8 string and is allocated a unique ID when created. The pool can be referred to by its name or by its ID. A pool may be renamed but the unique ID is always fixed.

1.4.1 Commit Objects

Data is added into a pool in atomic units called commit objects.

Each commit object is assigned a global ID. Similar to Git, Zed commit objects are arranged into a tree and represent the entire commit history of the lake.

Technically speaking, Git can merge from multiple parents and thus Git commits form a directed acyclic graph instead of a tree; Zed does not currently support multiple parents in the commit object history.

A branch is simply a named pointer to a commit object in the Zed lake and like a pool, a branch name can be any valid UTF-8 string. Consistent updates to a branch are made by writing a new commit object that points to the previous tip of the branch and updating the branch to point at the new commit object. This update may be made with a transaction constraint (e.g., requiring that the previous branch tip is the same as the commit object's parent); if the constraint is violated, then the transaction is aborted.

The working branch of a pool may be selected on any command with the -use option or may be persisted across commands with the use command so that -use does not have to be specified on each command-line. For interactive workflows, the use command is convenient but for automated workflows in scripts, it is good practice to explicitly specify the branch in each command invocation with the -use option.

1.4.2 Commitish

Many zed commands operate with respect to a commit object. While commit objects are always referenceable by their commit ID, it is also convenient to refer to the commit object at the tip of a branch.

The entity that represents either a commit ID or a branch is called a commitish. A commitish is always relative to the pool and has the form:

  • <pool>@<id> or
  • <pool>@<branch>

where <pool> is a pool name or pool ID, <id> is a commit object ID, and <branch> is a branch name.

In particular, the working branch set by the use command is a commitish.

A commitish may be abbreviated in several ways where the missing detail is obtained from the working-branch commitish, e.g.,

  • <pool> - When just a pool name is given, then the comittish is assumed to be <pool>@main.
  • @<id> or <id>- When an ID is given (optionally with the @ prefix), then the commitish is assumed to be <pool>@<id> where <pool> is obtained from the working-branch commitish.
  • @<branch> - When a branch name is given with the @ prefix, then the commitish is assumed to be <pool>@<id> where <pool> is obtained from the working-branch commitish.

An argument to a command that takes a commit object is called a commitish since it can be expressed as a branch or as a commit ID.

1.4.3 Pool Key

Each data pool is organized according to its configured pool key, which is the sort key for all data stored in the lake. Different data pools can have different pool keys but all of the data in a pool must have the same pool key.

As pool data is often comprised of Zed records (analogous to JSON objects), the pool key is typically a field of the stored records. When pool data is not structured as records/objects (e.g., scalar or arrays or other non-record types), then the pool key would typically be configured as the special value this.

Data can be efficiently scanned via ranges of values conforming to the pool key.

The pool key will also serve as the primary key for the forthcoming CRUD semantics.

A pool also has a configured sort order, either ascending or descending and data is organized in the pool in accordance with this order. Data scans may be either ascending or descending, and scans that follow the configured order are generally more efficient than scans that run in the opposing order.

Scans may also be range-limited but unordered.

Any data loaded into a pool that lacks the pool key is presumed to have a null value with regard to range scans. If large amounts of such "keyless data" are loaded into a pool, the ability to do range scans over such data is impaired.

1.5 Time Travel

Because commits are transactional and immutable, a query sees its entire data scan as a fixed "snapshot" with respect to the commit history. In fact, Zed's from operator allows a commit object to be specified with the @ suffix to a pool reference, e.g.,

zed query 'from logs@1tRxi7zjT7oKxCBwwZ0rbaiLRxb | ...'

In this way, a query can time-travel through the commit history. As long as the underlying data has not been deleted, arbitrarily old snapshots of the Zed lake can be easily queried.

If a writer commits data after and while a reader is scanning, then the reader does not see the new data since it's scanning the snapshot that existed before these new writes occurred.

Also, arbitrary metadata can be committed to the log as described below, e.g., to associate index objects or derived analytics to a specific journal commit point potentially across different data pools in a transactionally consistent fashion.

While time travel through commit history provides one means to explore past snapshots of the commit history, another means is to use a timestamp. Because the entire history of branch updates is stored in a transaction journal and each entry contains a timestamp, branch references can be easily navigated by time. For example, a list of branches of a pool's past can be created by scanning the internal "pools log" and stopping at the largest timestamp less than or equal to the desired timestamp. Then using that historical snapshot of the pools, a branch can be located within the pool using that pool's "branches log" in a similar fashion, then its corresponding commit object can be used to construct the data of that branch at that past point in time.

Note that time travel using timestamps is a forthcoming feature.

1.6 Search Indexes

Unlike traditional indexing systems based on an inverted-keyword index, indexing in Zed is decentralized and incremental. Instead of rolling up index data structures across many data objects, a Zed lake stores a small amount of index state for each data object. Moreover, the design relies on indexes only to enhance performance, not to implement the data semantics. Thus, indexes need not exist to operate a lake and can be incrementally added or deleted without large indexing jobs needing to rebuild a monolithic index after each configuration change.

To optimize pool scans, the lake design relies on the well-known pruning concept to skip any data object that the planner determines can be skipped based on one or more indexes of that object. For example, if an object has been indexed for field "foo" and the query

foo == "bar" | ...

is run, then the scan will consult the "foo" index and skip the data object if the value "bar" is not in that index.

Also, each data object is broken up into seekable chunks and the chunk location of each index value is stored in the index so that only parts of large data objects need to be scanned based on this information.

This approach works well for "needle in the haystack"-style searches. When a search hits every object, this style of indexing would not eliminate any objects and thus does not help nor does any such indexing scheme.

While an individual index lookup involves latency to cloud storage to lookup a key in each index, each lookup is cheap and involves a small amount of data and the lookups can all be run in parallel, even from a single node, so the scan schedule can be quickly computed in a small number of round-trips (that navigate very wide B-trees) to cloud object storage or to a cache of cloud objects.

Future plans for indexing include full-text keyword indexing and type-based indexing (e.g., index all values that are IP addresses including values inside arrays, sets, and sub-records).

1.6.1 Index Rules

Indexes are created and managed with one or more index rules.

While you can simply create rules and run zed index update to ensure that indexes are all up to date with committed data, the process here involves indexing each data object and storing its index object as another cloud object in the data pool. Once an index is successfully computed, the binding between a data object and its index is transactionally committed to its branch so that the query planner always has a consistent view of the index relative to the data.

When data is merged from one branch to another, the indexes are retained and need not be recomputed.

Rules are organized into groups by name and defined at the lake level so that any named group of rules can be applied to data objects from any pool. The group name provides no meaning beyond a reference to a set of index rules at any given time.

When rules are created or changed, indexes may be updated simply by running the index update command.

1.6.2 Indexing Workflows

Indexes are all created and managed explicitly via the zed index commands and equivalent API endpoints. It is the responsibility of external agents to create indexes that can be utilized by the service. This design allows the indexing system to be scaled out and run independently from the ingest and query functions and be tailored to diverse workloads, e.g., the needs of a real-time log search use case are very different from those of an ETL use case but this design allows different workloads like these to be custom tuned.

Agents to perform automatic indexing are under development.

2. Zed Commands

The zed command is structured as a primary command consististing of a large number of interrelated sub-commands, similar to the docker or kubectl commands.

The following sections describe each of the available commands, but built-in help is also available:

  • zed -h with no args displays a list of zed commands.
  • zed command -h, where command is a sub-command, displays help for that sub-command.
  • zed command sub-command -h displays help for a sub-command of a sub-command and so forth.

2.1 Auth

zed auth login|logout|method|verify

Access to a Zed lake can be secured with Auth0 authentication. Please reach out to us on our Brim community Slack if you'd like help setting this up and trying it out.

2.2 Branch

zed branch [options] [name]

The branch command creates a branch with the name name that points to the tip of the working branch or, if the name argument is not provided, lists the existing branches of the selected pool.

For example, this branch command

zed branch -use logs@main staging

creates a new branch called "staging" in pool "logs", which points to the same commit object as the "main" branch. Once created, commits to the "staging" branch will be added to the commit history without affecting the "main" branch and each branch can be queried independently at any time.

Supposing the main branch of logs was already the working branch, then you could create the new branch called "staging" by simply saying

zed branch staging

Likewise, you can delete a branch with -d:

zed branch -d staging

and list the branches as follows:

zed branch

2.3 Create

zed create [-orderby key[,key...][:asc|:desc]] <name>

The create command creates a new data pool with the given name, which may be any valid UTF-8 string.

The -orderby option indicates the pool key that is used to sort the data in lake, which may be in ascending or descending order.

If a pool key is not specified, then it defaults to the special value this.

A newly created pool is initialized with a branch called main.

Zed lakes can be used without thinking about branches. When referencing a pool without a branch, the tooling presumes the "main" branch as the default, and everything can be done on main without having to think about branching.

2.4 Delete

zed delete [options] <id> [<id>...]
zed delete [options] -where <filter>

The delete command removes one or more data objects indicated by their ID from a pool. This command simply removes the data from the branch without actually deleting the underlying data objects thereby allowing time travel to work in the face of deletes.

If the -where flag is specified, delete will remove all values for which the provided filter expression is true. The filter expression must be a single comparison against the pool key using <, <=, > or >= (e.g., -where 'ts <= now() - 3h').

A vacuum command to delete permanently from a pool is under development.

2.5 Drop

zed drop [options] <name>|<id>

The drop command deletes a pool and all of its constituent data. As this is a DANGER ZONE command, you must confirm that you want to delete the pool to proceed. The -f option can be used to force the deletion without confirmation.

2.6 Index

zed index [options] apply|create|drop|ls|update

The index command has a number of sub-commands to create, manage, and delete indexing rules and apply these rules to create indexes of data objects.

2.6.1 Index Apply

zed index apply [options ]<rule> <id> [<id>, ...]

The index apply command applies the indexing rules defined by the index name <rule> to one or more data object IDs given by the <id> arguments to create new index objects.

The new objects are recorded in a new commit object in the working branch (or in the branch indicated with the -use option.) The options used to set metadata in the load command may also be specified here.

2.6.2 Index Create

zed index create <rule> field <field>

The index create command creates a field rule under the group of rules called <rule> for the field referenced by <field>, which should be an identifier or dotted-field path.

For example,

zed index create IndexGroupExample field foo

adds a field rule for field foo to the index group named IndexGroupExample. This rule can then be applied to a data object having a given <id> in a pool, e.g.,

zed index apply -use logs@main IndexGroupExample <id>

The index is created and transactionally added to the working branch's commit history so it becomes available to the query optimizer.

2.6.3 Index Drop

zed index drop <id> [<id> ...]

The index drop command deletes one or more index rules specified by <id>. Once deleted, no more indexes will be created for that rule but the underlying indexes are not actually deleted from the lake.

Commands to delete the underlying indexes and data from a lake are under development.

2.6.4 Index Ls

zed index ls [options]

The index ls command lists the indexes organized by groups that are configured in the lake.

2.6.5 Index Update

zed index update [rule [rule ...]]

The index update command creates index objects for all data objects in the working branch (or the branch specified by -use) that do not have an index object for the list of index rules given.

If no index rules are given, the update is performed for all index rules.

2.7 Init

zed init [path]

A new lake is initialized with the init command. The path argument is a storage path and is optional. If not present, the path is taken from the ZED_LAKE environment variable, which must be defined.

If the lake already exists, init reports an error and does nothing.

Otherwise, the init command writes the initial cloud objects to the storage path to create a new, empty lake at the specified path.

2.8 Load

zed load [options] input [input ...]

The load command commits new data to a branch of a pool.

Run zed load -h for a list of command-line options.

Note that there is no need to define a schema or insert data into a "table" as all Zed data is self describing and can be queried in a schema-agnostic fashion. Data of any shape can be stored in any pool and arbitrary data shapes can coexist side by side.

As with zq, the input arguments can be in any supported format and the input format is auto-detected if -i is not provided. Likewise, the inputs may be URLs, in which case, the load command streams the data from a Web server or S3 and into the lake.

When data is loaded, it is broken up into objects of a target size determined by the pool's threshold parameter (which defaults 500MiB but can be configured when the pool is created). Each object is sorted by the pool key but a sequence of objects is not guaranteed to be globally sorted. When lots of small or unsorted commits occur, data can be fragmented impacting performance.

Note that data is easily compacted by reading from a fragmented pool and writing it back to a target pool so that it is globally sorted and compacted into contiguous large objects. We will soon introduce a compaction feature that does this automatically inside of a pool and can either be run manually or configured to run automatically by the server.

For example, this command

zed load sample1.json sample2.zng sample3.zson

loads files of varying formats in a single commit to the working branch.

Parquet and ZST formats are not auto-detected so you must currently specify -i with these formats, e.g.,

zed load -i parquet sample4.parquet
zed load -i zst sample5.zst

An alternative branch may be specified with a branch reference with the -use option, i.e., <pool>@<branch>. Supposing a branch called live existed, data can be committed into this branch as follows:

zed load -use logs@live sample.zng

Or, as mentioned above, you can set the default branch for the load command via use:

zed use logs@live
zed load sample.zng

During a load operation, a commit is broken out into units called data objects where a target object size is configured into the pool, typically 100MB-1GB. The records within each object are sorted by the pool key. A data object is presumed by the implementation to fit into the memory of an intake worker node so that such a sort can be trivially accomplished.

Data added to a pool can arrive in any order with respect to the pool key. While each object is sorted before it is written, the collection of objects is generally not sorted.

Each load operation creates a single commit object, which includes:

  • an author and message string,
  • a timestamp computed by the server, and
  • an optional metadata field of any Zed type expressed as a ZSON value. This data has the Zed type signature:
author: string,
date: time,
message: string,
meta: <any>

where <any> is the type of any optionally attached metadata . For example, this command sets the author and message fields:

zed load -user -message "new version of prod dataset" ...

If these fields are not specified, then the Zed system will fill them in with the user obtained from the session and a message that is descriptive of the action.

The date field here is used by the Zed lake system to do time travel through the branch and pool history, allowing you to see the state of branches at any time in their commit history.

Arbitrary metadata expressed as any ZSON value may be attached to a commit via the -meta flag. This allows an application or user to transactionally commit metadata alongside committed data for any purpose. This approach allows external applications to implement arbitrary data provenance and audit capabilities by embedding custom metadata in the commit history.

Since commit objects are stored as Zed, the metadata can easily be queried by running the log -f zng to retrieve the log in ZNG format, for example, and using zq to pull the metadata out as in:

zed log -f zng | zq 'has(meta) | yield {id,meta}' -

2.9 Log

zed log [options] [commitish]

The log command, like git log, displays a history of the commit objects starting from any commit, expressed as a commitish. If no argument is given, the tip of the working branch is used.

Run zed log -h for a list of command-line options.

To understand the log contents, the load operation is actually decomposed into two steps under the covers: an "add" step stores one or more new immutable data objects in the lake and a "commit" step materializes the objects into a branch with an ACID transaction. This updates the branch pointer to point at a new commit object referencing the data objects where the new commit object's parent points at the branch's previous commit object, thus forming a path through the object tree.

The log command prints the commit ID of each commit object in that path from the current pointer back through history to the first commit object.

A commit object includes an optional author and message, along with a required timestamp, that is stored in the commit journal for reference. These values may be specified as options to the load command, and are also available in the API for automation.

Note that the branchlog meta-query source is not yet implemented.

2.10 Merge

Data is merged from one branch into another with the merge command, e.g.,

zed merge -use logs@updates main

where the updates branch is being merged into the main branch within the logs pool.

A merge operation finds a common ancestor in the commit history then computes the set of changes needed for the target branch to reflect the data additions and deletions in the source branch. While the merge operation is performed, data can still be written concurrently to both branches and queries performed and everything remains transactionally consistent. Newly written data remains in the branch while all of the data present at merge initiation is merged into the parent.

This Git-like behavior for a data lake provides a clean solution to the live ingest problem. For example, data can be continuously ingested into a branch of main called live and orchestration logic can periodically merge updates from branch live to branch main, possibly compacting and indexing data after the merge according to configured policies and logic.

2.11 Query

zed query [options] <query>

The query command runs a Zed program with data from a lake as input. A query typically begins with a from operator indicating the pool and branch to use as input. If from is not present, then the query reads from the working branch.

The pool/branch names are specified with from at the beginning of the Zed query along with an optional time range using range and to.

As with zq, the default output format is ZSON for terminals and ZNG otherwise, though this can be overridden with -f to specify one of the various supported output formats.

If a pool name is provided to from without a branch name, then branch "main" is assumed.

This example reads every record from the full key range of the logs pool and sends the results to stdout.

zed query 'from logs'

We can narrow the span of the query by specifying the key range, where these values refer to the pool key:

zed query 'from logs range 2018-03-24T17:36:30.090766Z to 2018-03-24T17:36:30.090758Z'

These range queries are efficiently implemented as the data is laid out according to the pool key and seek indexes keyed by the pool key are computed for each data object.

Lake queries also can refer to HEAD (i.e., the branch context set in the most recent use command) either implicitly by omitting the from operator:

zed query '*'

or by referencing HEAD:

zed query 'from HEAD'

When querying data to the ZNG output format, output from a pool can be easily piped to other commands like zq, e.g.,

zed query -f zng 'from logs' | zq -f table 'count() by field' -

Of course, it's even more efficient to run the query inside of the pool traversal like this:

zed query -f table 'from logs | count() by field'

By default, the query command scans pool data in pool-key order though the Zed optimizer may, in general, reorder the scan to optimize searches, aggregations, and joins. An order hint can be supplied to the query command to indicate to the optimizer the desired processing order, but in general, sort operators should be used to guarantee any particular sort order.

Arbitrarily complex Zed queries can be executed over the lake in this fashion and the planner can utilize cloud resources to parallelize and scale the query over many parallel workers that simultaneously access the Zed lake data in shared cloud storage (while also accessing locally- or cluster-cached copies of data).


Commit history, metadata about data objects, lake and pool configuration, etc. can all be queried and returned as Zed data, which in turn, can be fed into Zed analytics. This allows a very powerful approach to introspecting the structure of a lake making it easy to measure, tune, and adjust lake parameters to optimize layout for performance.

These structures are introspected using meta-queries that simply specify a metadata source using an extended syntax in the from operator. There are three types of meta-queries:

  • from :<meta> - lake level
  • from pool:<meta> - pool level
  • from pool@branch<:meta> - branch level

<meta> is the name of the metadata being queried. The available metadata sources vary based on level.

For example, a list of pools with configuration data can be obtained in the ZSON format as follows:

zed query -Z "from :pools"

This meta-query produces a list of branches in a pool called logs:

zed query -Z "from logs:branches"

Since this is all just Zed, you can filter the results just like any query, e.g., to look for particular branch:

zed query -Z "from logs:branches |'main'"

This meta-query produces a list of the data objects in the live branch of pool logs:

zed query -Z "from logs@live:objects"

You can also pretty-print in human-readable form most of the metadata Zed records using the "lake" format, e.g.,

zed query -f lake "from logs@live:objects"

2.12 Rename

zed rename <existing> <new-name>

The rename command assigns a new name <new-name> to an existing pool <existing>, which may be referenced by its ID or its previous name.

2.13 Serve

zed serve [options]

The serve command implements Zed's server personality to service requests from instances of Zed's client personality. It listens for Zed lake API requests on the interface and port specified by the -l option, executes the requests, and returns results.

2.14 Use

zed use [<commitish>]

The use command sets the working branch to the indicated commitish. When run without a commitish argument, it displays the current commitish in use.

For example,

zed use logs

provides a "pool-only" commitish that sets the working branch to logs@main.

If a @branch or commit ID are given without a pool prefix, then the pool of the commitish previously in use is presumed. For example, if you are on logs@main then run this command:

zed use @test

then the working branch is set to logs@test.

To specify a branch in another pool, simply prepend the pool name to the desired branch:

zed use otherpool@otherbranch

This command stores the working branch in $HOME/.zed_head.