SSTable Interpretation

Topic: Internals

Audience: Devops professionals, architects

The SSTables Data File contains rows of data. This document discusses how to interpret the various fields described in SSTables Data File in the context of Scylla, and how to convert this data into Scylla’s native data structure: mutation_partition.

SSTable Rows

Each row in the SSTable isn’t necessarily a full row of data. Rather, it is just a mutation, a list of changed (added or deleted) columns and their new values (or “tombstone” for a deleted column), and a timestamp for each such change (this timestamp is used for reconciling conflicting mutations). The full data row needed by a request will be composed from potentially multiple sstables and/or the in-memory table(s).

As we’ll explain below when discussing clustering columns, the best term for what we read from one row in the SSTable isn’t a “row”, but rather a partition.

For these reasons, Scylla’s internal representation for a row we read from the SSTable is called class mutation_partition.

Column Names

As explained in SSTables Data File, the sstable row (a mutation partition) is a list of cells (column values). Each cell is preceded by the full column name. This was considered a good idea when Apache Cassandra was designed to support rows with many and arbitrary columns, but Scylla is more oriented toward the CQL use case with a known schema. So Scylla’s rows do not store the full column name, but rather store a numeric ID which points to the known list of columns from the schema. So as we read column names from the SSTable in form of IDs, we need to translate the IDs into names by looking them up in the schema.

Composite Column Names

But the column names mentioned in the sstable cells usually aren’t a field name mentioned in the schema, and additional processing needs to be done to the column names which we read from the SSTable.

The first issue is that starting with Apache Cassandra 1.2 (and unless WITH COMPACT STORAGE is used), column names aren’t plain strings, but each is rather rather a composite name - a short list of name components. See SSTables Data File for this list’s on-disk encoding, but for simplicity of exposition let’s use the convention of writing a composite name as (part1:part2:…)

Things are simplest when no clustering keys are involved; Then, cell names have just one component. For example, consider the table created with the following CQL command:

        name text,
        age int,
        PRIMARY KEY (name)
INSERT INTO harels (name, age) VALUES ('nadav', 40);

In this table, we have a row with the key “nadav”, and in this row, one cell, with the column name “age” encoded as a single-component composite string (age) (on disk, \003 a g e \0). This only component, “age”, can be looked up in the table’s schema, and converted to a column ID in the mutation_partition, as explained above.

CQL Row Marker

As explained in SSTables Data File, Apache Cassandra always (except when a COMPACT table is used and other esoteric exceptions) adds to each row an empty-named and empty-valued bogus “cell”, to solve various problems involving finding a row after its only column has been deleted. This is also explained in a comment in UpdateStatement.Java, and in

For example, if we inspect with tools/bin/sstable2json the table created in the previous example, we find:

{"key": "nadav",
 "cells": [["","",1426688662900463],

The first cell, with the empty name (“”) and value (the second “”), is the “CQL Row Marker”. As usual, the empty name “” shown by sstable2json is not actually an empty string, but a composite with one empty component, () (serialized on disk as '\000 \000 \000').

I hope we can simply ignore these CQL Row Marker cells, and not duplicate them in Scylla’s internal format. We just need a different way to allow empty rows (a row with only a key, but no data columns) to exist, to circumvent the problems mentioned in CASSANDRA-4361 and the comment in UpdateStatement.Java.

Clustering Keys

When the table has a clustering key, column names in the sstable no longer have a single component:

USE try1;
CREATE TABLE harels2 (
        name text,
        nick text,
        age int,
        PRIMARY KEY (name, nick)
) WITH compression = {};
INSERT INTO harels2 (name, nick, age) VALUES ('nadav', 'nyh', 40);

Note how name and nick form the primary key, but the CQL syntax specifies that the partition key is name, and the clustering key is nick. This means that different tables entries that have the same name (but different nick) will appear in the same partition, i.e., in the same sstable row. Inside that partition, different nicks can appear, each with its own age. To see what this looks like in the sstable we again use the sstable2json tool, and see:

{"key": "nadav",
 "cells": [["nyh:","",1427032626839065],

In other words, the composite column name (nyh:age) is used to store the age for the nick nyh, and a different column would be used to store some other nick’s age. Note how in (nyh:age), the “nyh” is not one of the CQL column names, but rather the value of the clustering column nick, and only the last component, “age”, is an actual name of a field from the CQL schema.

In Scylla nomenclature, this single partition (with key name=”nadav”) has multiple rows, each with a different value of the clustering key (nick). Each of these rows has, as usual, columns whose names are the fields from the CQL schema (and as explained above, are kept as column IDs, not names).

So when converting an sstable row into a mutation_partition, we need to consult the schema to look for a clustering key. If “nick” is the clustering key, we shouldn’t look as usual for cells named (nick). Instead, we expect every cell name to have >=2 components, where the first component is a value of nick, and the second component an actual column name. In the mutation_partition object, we need to insert multiple row objects, where each row corresponds to one value of the first component.

The silly cell with key (nyh:) (empty second component) and empty value is the “CQL Row Marker” described above, which appears for each row (combination of partition key and clustering key) separately.

Static Columns

A static column is a special column which is shared by all rows of the same partition. As we saw above, the case of multiple rows per partition happens when there is a clustering column. When Datastax introduced static columns in Cassandra 2.0.6, they used the following example (

     user text,
     balance int static,
     expense_id int,
     amount int,
     PRIMARY KEY (user, expense_id)

This is a table of bills (amounts that certain users need to pay). According to the “PRIMARY KEY” line, the partition key is “user”, and “expense_id” is the clustering key; This means that there will be a partition (sstable row) for each user, and for each such partition, we can have multiple expenses (rows), each with a different clustering-key expense_id, and corresponding amount. But the “balance” column is for all the different expenses of the same user.

So if we insert one expense for ‘user1’, and set user1’s balance:

INSERT INTO bills (user, balance) VALUES ('user1', 17);
INSERT INTO bills (user, expense_id, amount) VALUES ('user1', 1, 8);

What is written to the sstable looks like this (as usual, output from sstable2json):

{"key": "user1",
 "cells": [[":balance","17",1428849747953348],

The (1:amount) and (1:) is what we already saw above, the new thing here is the (:balance), a static column.

So sstables have static columns specially marked by an empty first component of the composite cell name. We need to verify that each such cell actually corresponds to a known static column from the table’s schema, and collect all these static columns into one row (_static_row) stored in Scylla’s mutation_partition.

TODO: explains that static columns do not really have an empty first component (size 0), but rather the first component has the fake size STATIC_MARKER = 0xFFFF (65536). We need to verify this.

Compound Clustering Key

When the clustering key is compound, i.e., composed of multiple columns, the SSTable column names will contain more than two components. For example consider:

USE try1;
     user text,
     expense_id int,
     year int,
     amount int,
     PRIMARY KEY (user, year, expense_id)
INSERT INTO bills3 (user, year, expense_id, amount) VALUES ('user1', 2015, 1, 8);

Here as usual, the first column name in “PRIMARY KEY”, user, is the partition key, but the two others, year and expense_id are both clustering columns, forming a compound clustering key. I.e., each partition contains several rows, each defined by (and sorted by) the pair (year, expense_id).

The resulting SSTable row is:

{"key": "user1",
 "cells": [["2015:1:","",1428853746711253],

Note how the column name “amount” is now prefixed by two components, the values of the two clustering columns. Of course, a schema can have any number of clustering columns and as a result, expect that number of prefix components in the SSTable’s column names.

As before, all but the last column-name component are expected to be values of the clustering-key of the various rows inside the partition, and only the last component is a column name to be looked up in the schema. It’s safer, though, to consult with the schema to see the number of clustering columns instead of guessing it as the number of components minus one. This is a good sanity check, and also necessary when collections are concerned (see below).

Compound Partition Key

The row key read from the SSTable can also be a composite (a list of components) if the schema says the partition key is compound. For example:

     user text,
     expense_id int,
     amount int,
     PRIMARY KEY ((user, expense_id))
INSERT INTO bills (user, expense_id, amount) VALUES ('user1', 1, 8);

Note the extra pair of parenthesis in the “PRIMARY KEY” specification, which says that expense_id is part of the partition key, not a clustering ey.

The key of each SSTable row is now the pair (user, expense_id), a composite with two components.

TODO: Print the resulting SSTable


The encoding of collections in SSTables is more complex.

Consider this table definition with a column which is a set collection:

     user text,
     favorites set<text>,
     PRIMARY KEY (user)
INSERT INTO col2 (user, favorites) VALUES ('user1', {'raindrops', 'kittens'});

The resulting SSTable row is:

{"key": "user1",
 "cells": [["","",1428855312063525],

Here we also have two components in the column names, but we need to know this is not the case of a clustering key (“favorites” isn’t a value of a clustering column) but that of a collection. We need to consult the schema to tell the two cases apart (in this case, there are no clustering columns, so no component needs to be treated as a clustering column, so when we see two components, it must be a collection).

In a set, we have a cell for each item in the collection, and second component of the cell’s name is the serialized value. E.g., in our case, the byte array “kittens” is shown by sstable2json in hex (6b697474656e73) - in the actual SSTable the hex does not appear (there is the length of the string followed by the actual bytes). The value of each of these cells is empty for a set (for other types of collections it is not empty - see below).

The weird cell in the beginning of the above sstable2json output (with favorites:_) is not a normal cell - this is how sstable prints a range tombstone, whose range is between the start of “favorites:” and the end of “favorites:”, the markedForDeleteAt value is 1428855312063524 and localDeletionTime is 1428855312. The need for this range tombstone appears to be as follows: Because each of the collection’s items is a separate cell, when we set the collection (as in the INSERT command we used) the intention is to delete any old item in the collection, if there are any, and add the new items. The range tombstone takes care of deleting all the old items.

The actual sstable doesn’t have the “_” or “!” characters printed by sstable2json. What it really has is "\00\09favorites\ff" and 0`9favorites:raw-latex:0`1”. I.e., each of these two column names has only one component, but instead of ending as usual with the end-of-component byte :raw-latex:0`0, the first ends with 0`1 (END). This means the range tombstone spans everything between the first column and the last, as indeed desired.

The second type of collection, the map, is similar to the set, just the value is not empty but rather the desired value in the map. For example,

     user text,
     favorites map<text,int>,
     PRIMARY KEY (user)
) WITH compression = {};
INSERT INTO col4 (user, favorites) VALUES ('user1', {'raindrops' : 1, 'kittens' : 2});

We see in the SSTable with sstable2json:

{"key": "user1",
 "cells": [["","",1428864848550739],

I.e., indeed exactly the same as the representation of the set, except the cell’s values are the desired 1 and 2. The way the values are printed above as strings (“0000002”) is just an artifact of how sstable2json works - the value is represented in the SSTable as an actual serialized int (32-bit length 4, followed by 4 bytes of the integer’s representation) as expected.

For the ordered list collection, things are similar, but not quite the same because of the need to keep the desired item order:

     user text,
     favorites list<text>,
     PRIMARY KEY (user)
INSERT INTO col1 (user, favorites) VALUES ('user1', ['raindrops', 'kittens']);

The resulting SSTable row is now:

{"key": "user1",
 "cells": [["","",1428854738475900],

Note how this time, the items (“raindrops” and “kittens”) are the value of the cell, not in the column name. In the column name we have some long strings intended to sort in the requested list order. These long hex strings are misrepresented by sstable2json - they are not a hex string but rather a 16-byte UUID.

To merely keep the list items in order, Apache Cassandra could have used small integers instead of these UUIDs. But these UUIDs have an additional benefit: as explains, Apache Cassandra wishes to allow efficient append and prepend operations to an existing list - without reading the existing list first (i.e., the append/prepend is a fast write-only mutation, not a slow read-modify-write operation). To achieve that, Apache Cassandra uses signed time-UUIDs as the list sort string - with positive times used for append operations, and negative for prepend operations. This ensures that, for example, a later append always sorts after an earlier append - without the append having to know which items already exist in the list.

Scylla’s internal storage of a collection in a mutation is the class collection_mutation, and we need to convert the above described representation into that class. TODO: I still can’t figure out exactly what is the internal structure of our collection_mutation (which hides behind an opaque byte array), or what functions we are supposed to call to build one).

Cells with Expiration Time

An SSTable cell may have an expiration time, as explained Such a cell is marked by the EXPIRATION_MASK bit in the mask byte, and in addition to the cell’s normal fields, has two additional fields, “ttl” and “expiration”, both 32-bit and measured in seconds. “ttl” is the original time-to-live (number of seconds until expiration) specified when the cell was created, and “expiration” is the absolute time when the cell should be expired (in seconds since the Unix Epoch).

The following CQL example creates a cell with a time-to-live of 3600 seconds:

    name text,
    age int,
    PRIMARY KEY (name)
INSERT INTO ttl (name, age) VALUES ('nadav', 40) USING TTL 3600;

sstable2json prints the resulting SSTable row as:

{"key": "nadav",
 "cells": [["","",1430151018675502,"e",3600,1430154618],

Note how each of cells (the cql row marker, and our actual data cell) have a TTL of 3600 seconds, and an expiration time calculated by adding 3600 to the current time on the server. The fact that the row marker also got this TTL is not certain to be a good thing - see discussion in

Cell Tombstone

We’ve already discussed row tombstones (marking the deletion of an entire row) and range tombstone (marking the deletion of a range of columns). We can also have a cell tombstone, marking the deletion of a single cell. A deleted cell is encoded in the SSTable like a normal cell, except the mask has the DELETION_MASK bit, and the value of the cell contains the serialized local_deletion_time (the local server time in seconds since the epoch), which is probably only needed for the purpose of purging the tombstone after gc_grace_seconds have elapsed.

To create an sstable with a deleted cell in Apache Cassandra, consider creating a table with some data:

CREATE TABLE deleted (
    name text,
    age int,
    PRIMARY KEY (name)
INSERT INTO deleted (name, age) VALUES ('nadav', 40);

Then flushing this data to an SSTable (with bin/nodetool flush keyspacename), and then deleting the cell we added:

DELETE age FROM deleted WHERE name = 'nadav';

When the second SSTable is flushed, it will have a cell tombstone. sstable2json shows the second SSTable like this:

{"key": "nadav",
 "cells": [["age",1430200516,1430200516937621,"d"]]}

Note how the cell has the DELETION_MASK bit (written as a “d”), its “value” is the local-deletion-time, 1430200516, and as usual it has a timestamp

Scylla Architecture


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