Exploring Query Plan Scan Nodes with SQL EXPLAIN

Bolek Ziobrowski

Bolek Ziobrowski

QuestDB Team
Image of a hard disk drive.

Welcome to another post on SQL performance tuning! Previously, in EXPLAIN Your SQL Query Plan, we learned how to use EXPLAIN to check the estimated execution plan of a query. This time we'll focus on the basic methods the QuestDB SQL engine uses to read data - scan nodes.

So, what is a scan node?

A scan node is a query execution plan node responsible for fetching data according to an algorithm.

Imagine binary search on sorted data but with partitions, indexes, and column data files instead of arrays.

Most databases refer to scan nodes using custom terms, like "operations" or "select types", while in literature, it's usually "access methods".

The QuestDB SQL engine uses the following scan nodes:

  • Frame scan
  • Interval scan
  • Index scan
    • Index scan in table order
    • Index scan in index order

All of the above support both forward and backward scanning.

Any non-trivial query is bound to use one of these, so as with traditional CS algorithms, it makes sense to learn about their characteristics to write more optimized SQL queries.

All the examples in this article use tables available in the QuestDB demo instance.

The following is the schema for the table trades:

CREATE TABLE trades (
symbol SYMBOL,
side SYMBOL,
price DOUBLE,
amount DOUBLE,
timestamp TIMESTAMP
) timestamp (timestamp) PARTITION BY DAY WAL;

Frame scan#

Frame scan.

Frame scan, also known as Full Table Scan, reads all table rows. With QuestDB's column-based storage model, the amount of data to read depends on the columns used in the query.

A query selecting all columns amounts to a traditional Full Table Scan. A query selecting a single/a few columns from a wide table might read just a few percent of the table's data.

Frame forward scans start at the first row of the oldest partition (the one with the lowest timestamp partition key value) and stop at the last row of the latest one (the one with the highest timestamp partition key value). Differentiating between forward and backward scans only makes sense for tables with a designated timestamp because they store data in that timestamp order. For tables without designated timestamps, the scan direction is not vital for query performance tuning because there's no predictable order to data.

Let's look at a simple SELECT statement:

EXPLAIN
SELECT * FROM trades
ORDER BY timestamp;
QUERY PLAN
DataFrame
    Row forward scan
    Frame forward scan on: trades

In the EXPLAIN output, the forward scan is represented on the table level as Frame forward scan, and on the partition level as Row forward scan.

Why not just Seq Scan like in PostgreSQL?

It might seem verbose to show a simple table scan with so many plan nodes, but there's a reason for that.

QuestDB is optimized to work with large time-series data sets stored in time-partitioned tables, often with tens or hundreds of partitions. If we were to follow PostgreSQL's approach of showing all partitions, for instance:

EXPLAIN
SELECT *
FROM trades
WHERE timestamp < now();

we would get:

QUERY PLAN
Append (cost=0.00..176.00 rows=2720 width=12)
-> Seq Scan on trades_y2006 trades_1 (cost=0.00..40.60 rows=680 width=12)
       Filter: (ts < now())
-> Seq Scan on trades_y2007 trades_2 (cost=0.00..40.60 rows=680 width=12)
       Filter: (ts < now())
...

This output would take too much space.

Therefore, QuestDB's EXPLAIN command shows:

  • The direction of Data Frame (a piece or the whole partition) iteration, e.g. Frame forward scan

  • The direction of Row iteration within a Data Frame, e.g. Row forward scan

  • A list of scan boundaries in Interval scan, e.g.:

    intervals: [("1970-01-01T00:00:00.000000Z","1970-01-01T23:59:59.999999Z")]

Opposite to Frame forward scans, Frame backward scans read all table rows from the latest partition and finish at the oldest partition.

Take another exaple:

EXPLAIN
SELECT * FROM trades
ORDER BY timestamp DESC;
QUERY PLAN
DataFrame
    Row backward scan
    Frame backward scan on: trades

The query optimizer implements ORDER BY with a backward scan. If you run the same query on a table without the designated timestamp, unsurprisingly, you'll get:

QUERY PLAN
Sort light
  keys: [timestamp desc]
    DataFrame
        Row backward scan
        Frame backward scan on: trades

Sorting is required here because the data is in an unknown order.

Interval scan#

Interval forward scan.

In addition to the more common Frame scans, QuestDB implements a more optimized type of table scan for queries with a condition on the designated timestamp. We call this "interval scan". The QuestDB engine analyzes the condition, extracts a list of timestamp intervals, and then for each interval, it binary searches scan boundaries in the designated timestamp column, for instance:

EXPLAIN
SELECT * FROM trades
WHERE timestamp IN '2023-01-20';
QUERY PLAN
DataFrame
    Row forward scan
    Interval forward scan on: trades
      intervals: [("2023-01-20T00:00:00.000000Z","2023-01-20T23:59:59.999999Z")]

As the plan shows, the query optimizer reduces scanning to a single interval, the 2023-01-20 day partition.

The engine might even detect conflicting conditions and not run any scan at all, e.g.:

EXPLAIN
SELECT * FROM trades
WHERE timestamp in '2023-01-20'
AND timestamp < '2022-01-01';
QUERY PLAN
Empty table

If the condition is too complex (especially if the query uses the designated timestamp as a function argument), the engine will fall back to the default table scan with a filter, e.g.:

EXPLAIN
SELECT * FROM trades
WHERE dateadd('m', 1, timestamp) in '2023-01-20';
QUERY PLAN
Async Filter
  filter: dateadd('m',1,timestamp) in [1674172800000000,1674259199999999]
  workers: 24
    DataFrame
        Row forward scan
        Frame forward scan on: trades

Rewriting the WHERE condition to the below makes the query use the interval scan:

timestamp BETWEEN '2023-01-19T23:59:00.000000Z' AND '2023-01-20T23:59:00.000000Z'

Similarly to Frame scans, QuestDB also supports Interval backward scans. They run in the reverse order from their forward counterpart: from the last row of the last interval to the first row of the first interval. The scan node can be used to implement descending timestamp order without the need to sort:

EXPLAIN
SELECT * FROM trades
WHERE timestamp in '2023-01-20'
ORDER BY timestamp DESC;
QUERY PLAN
DataFrame
    Row backward scan
    Interval backward scan on: trades
      intervals: [("2023-01-20T00:00:00.000000Z","2023-01-20T23:59:59.999999Z")]

Index scan#

In contrast to Frame and Interval scans, which access table data directly, index scans first scan an index and then use the result values - row ids - to access relevant table rows. Another difference is that index scans are performed separately for each partition or interval used by a query. That's why it appears in the same EXPLAIN output location as e.g. Row forward scan.

The trades table doesn't have any index, so let's switch to another demo table, pos, with the following schema:

CREATE TABLE pos (
time TIMESTAMP,
id SYMBOL INDEX,
lat DOUBLE,
lon DOUBLE,
geo6 GEOHASH(6c),
geo12 GEOHASH(12c)
) timestamp (time) PARTITION BY DAY;

Index scan with a single key#

Index scan.

For any given index key, row ids are stored in table order, which is the same as timestamp order for tables with the designated timestamp. It means that when querying for a single index key, the ordering can be implemented with the scan direction alone without the need for sorting:

EXPLAIN SELECT * FROM pos WHERE id in ('X');
EXPLAIN SELECT * FROM pos WHERE id in ('X') ORDER BY time;

The queries above produce the same plan:

QUERY PLAN
DeferredSingleSymbolFilterDataFrame
    Index forward scan on: id deferred: true
      filter: id='X'
    Frame forward scan on: pos

Note - deferred: true means that symbol is not found in the symbol dictionary, so the resolution was delayed until the query run time.

What if we switch the ORDER BY direction and add a condition on the timestamp?

EXPLAIN
SELECT * FROM pos
WHERE id in ('X')
AND time in '2023-02-01'
ORDER BY id, time DESC;

This yields:

QUERY PLAN
DeferredSingleSymbolFilterDataFrame
    Index backward scan on: id deferred: true
      filter: id='X'
    Interval forward scan on: pos
      intervals: [("2023-02-01T00:00:00.000000Z","2023-02-01T23:59:59.999999Z")]

As you can see, not only is the potential sort replaced by a backward scan, but the scanning was reduced to a single timestamp interval by combining an Index scan with an Interval scan.

Index scan with multiple keys#

Things get more interesting when there's more than one index key to scan. As shown in the index scan diagram above, indexes are partitioned. When QuestDB iterates over a partition or interval list and conducts an index scan for each partition or interval, the output is still in timestamp order.

This is not the case for an index scan with multiple key values because table rows associated with each index key can interleave. Reading all rows associated with key k1, then k2, etc. could end up jumping randomly over partition data.

We can scan row ids in different orders:

  • Table-order - scans the minimum row id available from all per-key row ids until there's none left.
  • Index-order - first reads all row ids associated with key k1, then k2, and so on.

Table order#

Index scan in table order.

Index scans with multiple keys in table order read all row ids associated in table (or physical) order. It means that memory and disk access is as sequential as possible, which is a good default approach. This scan node is comparable to PostgreSQL's Bitmap Heap Scan.

EXPLAIN
SELECT * FROM pos
WHERE id in ('X', 'Y');
QUERY PLAN
FilterOnValues
    Table-order scan
        Index forward scan on: id deferred: true
          filter: id='X'
        Index forward scan on: id deferred: true
          filter: id='Y'
    Frame forward scan on: pos

Index order#

Index scan in index order.

Index scans with multiple keys in index order scan table rows using row ids associated with the first index key, followed by those associated with the second index key, and so on.

The scan continues until reaching the last index value. It might be used to avoid sorting at the price of potentially more random memory and disk accesses.

EXPLAIN
SELECT * FROM pos
WHERE id in ('X', 'Y')
AND time in '2023-02-01'
ORDER BY id,time DESC;
QUERY PLAN
FilterOnValues symbolOrder: asc
    Cursor-order scan
        Index backward scan on: id deferred: true
          filter: id='X'
        Index backward scan on: id deferred: true
          filter: id='Y'
    Interval forward scan on: pos
      intervals: [("2023-02-01T00:00:00.000000Z","2023-02-01T23:59:59.999999Z")]

It may look like we're mixing Frame (Interval forward scan) and in-Frame (Index backward scan) scan directions, but in fact, it doesn't matter because there's just one partition to scan. While this makes sense with a single partition, with multiple partitions it would require sorting. That's because combining the results of Index scans for each partition is not guaranteed to produce the required order.

Without sorting, the output could look like the following:

timeid...
2023-02-01T01:00:00.000000ZX
2023-02-01T02:00:00.000000ZX
2023-02-01T01:00:00.000000ZY
2023-02-02T01:00:00.000000ZX
2023-02-02T02:00:00.000000ZY
...

More in-depth examples#

Now that we've learned the basics, let's dig a bit deeper.

The Frame scan is commonly known as the Full Table Scan. That's because scanning a table might have to, in the pessimistic case, read all table rows. For certain queries, however, it might be fine to read just a handful of rows. For example, say we need to count the number of rows in the trades table:

SELECT count(*) FROM trades;

it returns 1634599313 in just 240őľs.

Is the response time right? According to the execution plan:

QUERY PLAN
Count
    DataFrame
        Row forward scan
        Frame forward scan on: trades

It should be doing a full table scan, but the response time is way too fast for this to be possible. Reading all timestamp values, that is about 12GB of memory, in 240őľs would require 50TB/s bandwidth, way higher than the 'lousy' 20GB/s available on the demo instance. What actually happens?

If possible, instead of iterating over all records, the Count plan node iterates over partitions and sums the number of rows in each using partition metadata. This optimization only makes sense in the absence of the WHERE conditions.

For comparison, the following query has to evaluate the condition for each table row, and even with parallel execution, it still takes about 7 seconds to complete:

SELECT count(*)
FROM trips
WHERE total_amount > 0;

Now, let's check if any of the trips finish at a specific location. A simple way to phrase it is:

SELECT count(*)
FROM trips
WHERE dropoff_location_id = 110;

The query returns in 120 ms, which is nice, but can we make it faster? Since we're only interested in knowing if any trip meets the criteria, we don't really need the exact count. Instead, we can find the first matching row and stop:

SELECT count(*) FROM
(
SELECT *
FROM trips
WHERE dropoff_location_id = 110
LIMIT 1
);

This time, the query returns in 90 ms, which means that the first row is away from the start of the table, and the engine has to scan more than half of the table. What if we do a backward scan from the end? How 'full' would the scan be then? Would it be half full or half empty?

SELECT count(*) FROM
(
SELECT *
FROM trips
WHERE dropoff_location_id = 110
ORDER BY pickup_datetime DESC
LIMIT 1
);

It turns out that it's not full at all! The result comes ~ 12 times faster, in 10 ms. It makes sense because the designated timestamp value of the matching row is 2019-05-21T20:51:26.000000Z, very close to the maximum in the table - 2019-06-30T23:59:56.000000Z.

Let's check the execution plan:

QUERY PLAN
Count
    Async JIT Filter
      limit: 1
      filter: dropoff_location_id=110
      workers: 24
        DataFrame
        Row backward scan
        Frame backward scan on: trips

As expected, the query does a backward scan (Frame backward scan) and stops on the first matching row ( limit: 1 under Async JIT Filter).

Remember that the approach above only makes sense if the data is close to the end of the table; otherwise, it might slow things down.

Using EXPLAIN to optimize your SQL queries#

We have introduced the basic scan nodes available in QuestDB:

  • Frame scan
  • Interval scan
  • Index scan (in table and index order)

Peeking under the database's hood with EXPLAIN, we have also demonstrated some of the available optimizations for tables with the designated timestamp. Knowing how data is accessed, we were able to optimize queries by reducing the volume of read data.

In the examples above, we have learned methods such as:

  • Limiting the number of columns accessed
  • Reducing table scanning by adding conditions on the designated timestamp
  • Reducing table scanning with LIMIT and/or changing the scan direction
  • Accessing records via the index

Feel free to try out examples on the QuestDB demo instance. We hope you enjoy this tutorial.

Happy query tuning!

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