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Astronomical Data Analysis Software and Systems VII
ASP Conference Series, Vol. 145, 1998
R. Albrecht, R. N. Hook and H. A. Bushouse, e
Ö Copyright 1998 Astronomical Society of the Pacific. All rights reserved.
ds.
A Queriable Repository for HST Telemetry Data, a Case
Study in using Data Warehousing for Science and
Engineering
Joseph A. Pollizzi, III and Karen Lezon
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore,
MD 21218, Email: pollizzi@stsci.edu
Abstract. The Hubble Space Telescope (HST) generates on the order
of 7,000 telemetry values, many of which are sampled at 1Hz, and with
several hundred parameters being sampled at 40Hz. Such data volumes
would quickly tax even the largest of processing facilities. Yet the ability
to access the telemetry data in a variety of ways, and in particular, using
ad hoc (i.e., no a priori fixed) queries, is essential to assuring the long term
viability and usefulness of this instrument. As part of the recent NASA
initiative to re­engineer HST's ground control systems, a concept arose
to apply newly available data warehousing technologies to this problem.
The Space Telescope Science Institute was engaged to develop a pilot to
investigate the technology and to create a proof­of­concept testbed that
could be demonstrated and evaluated for operational use. This paper
describes this e#ort and its results.
1. HST as a Telemetry Source
The Hubble Space Telescope (HST) is well a known source of significant and sub­
stantial amounts of Astronomy data. Less known, however, is that the HST is
also one of the most highly instrumented non­manned platforms ever launched.
Over 6,000 telemetry points are monitored on the HST. These ``monitors'' cover
practically every aspect of the platform and of the various instrument environ­
mental and state conditions.
In addition to routine study and problem analysis, we use telemetry to look
for long term trends in how the platform is behaving. By carefully studying
such trends, we hope to uncover potential problems before they arise. In this
way, we plan to extend the scientific utility of this unique instrument as far as
possible through its planned lifetime (now scheduled through to 2010).
1.1. Complications in Querying Telemetry Values
Since telemetry values are sampled at widely di#erent rates, looking for a ``cause­
e#ect'' relationship between monitors can rarely be found by identifying time
matches between records. Rather, the queries tend to look for overlapping time
windows when the monitors acquire some state. We have coined the term,
``Fuzzy Query'' to describe this kind of query. Using a stylized SQL, a fuzzy
query typically appears as:
367

368 Pollizzi and Lezon
Select ParamA . . . ParamN where Param1 > somelimit AND Param2 >
somelimit AND Time(Param2) # Time(Param1) + someDelta
The underlined portion in the above query highlights this `fuzzy' aspect.
An SQL designer will recognize the complexity such a query requires.
1.2. Dealing with the Telemetry Volume
Then there's the shear volume of the telemetry data. At its nominal format
and rate, the HST generates over 3,000 monitored samples per second. Track­
ing each sample as a separate record would generate over 95 giga­records/year,
or assuming a 16 year Life­of­Mission (LOM), 1.5 tera­records/LOM. Assum­
ing a minimal 20 byte record per transaction yields 1.9 terabytes/year or 30
terabytes/LOM. Such volumes are supported by only the most exotic and ex­
pensive custom database systems made.
By careful study of the data, we discovered two properties that could signif­
icantly reduce this volume. First, instead of capturing each telemetry measure­
ment, by only capturing when the measurement changed value ­ we could reduce
the volume by almost 3­to­1. Second, we recognized that roughly 100 parame­
ters changed most often (i.e., high frequency parameters) and caused the largest
volume of the ``change'' records. By averaging these parameters over some time
period, we could still achieve the necessary engineering accuracy while again
reducing the volume of records. In total, we reduced the volume of data down
to a reasonable 250 records/sec or approximately 2.5 terabytes/LOM.
2. Data Warehousing as a Potential Solution
The complexities and expected volumes in dealing with telemetry data naturally
lead us to consider Data Warehousing. Beyond its data handling abilities, Data
Warehouses are designed to be balanced in their approach to the data. That is,
they expect to handle the ad­hoc type queries with little or no pre­knowledge of
what will be of interest.
2.1. What is Data Warehousing
From a User's perspective, a data warehouse looks very similar to a typical
relational database management system (RDBMS). A user will find a query
language very similar to (if not the same as) SQL. Typically, the warehouse
will support one or more programming interfaces, such as ODBC, which allows
the warehouse to be accessed by familiar reporting or analysis tools (such as
Microsoft's Access, IDL, PV­Wave, )
To the database designer, a di#erent perspective is shown. There is no
transaction or update facility for the warehouse. The warehouse operates either
with many readers or a single writer, and the warehouse is ``loaded'' as opposed
to being updated. In laying out the warehouse, the designer quickly learns
that their logical definition (i.e., the layout of the tables and attributes of the
warehouse) is more intimately tied to the physical definition (how the warehouse
is laid­out on the physical i/o subsystem). Choices in one will often significantly
a#ect the other.

A Queriable Repository for HST Telemetry Data 369
In giving up the flexibility in transactions and by having the closer linkage
between the physical and logical views, the warehouse provides a number of new
features particularly in supporting e#cient indices for very large data volumes.
2.2. The Star Index
One of the most common indexing methods that characterizes a data warehouse
is called the Star. In using a Star index, the designer starts by designing a few
number of fact tables.
Generally, a fact table can be viewed as a flat file version of an entire
collection of typical database tables. The goal here is not to normalize data.
Instead, a fact table attempts to bring together as many related attributes as
can be expressed in a single table with (e#ectively) an unlimited number of
columns.
While a fact table holds the records of interest to be searched, dimension
tables provide the meta­data that describes aspects of a fact table and supports
the rapid indexing of data. A dimension table can be formed for any column
within a fact table, when all possible values for that column can be taken from
a pre­defined set of values. For example, a column holding a person's age can
be reasonably limited to the set of whole integers from {1 . . . 150}; sex from {
``Male'', ``Female'' }. Even an arbitrary set of values is appropriate. Consider the
social security numbers for all the employees of a company. While the individual
numbers themselves may be an arbitrary string of digits, all the numbers are
known and can be listed within a dimension table.
The star index then relates a fact table to one or more corresponding di­
mension tables.
3. Resulting Fact Tables
Applying this strategy to the HST telemetry problem produced the following
Fact Tables:
. Telemetry Facts with the columns: Mnemonic, Format­type, Flags, Raw
Value, Start Time, Stop Time, Start Millsec, Discrete Value (NULL if the
telemetry parameter is not a discrete), and Engineering Units (EU) Value
(NULL if the telemetry parameter is a discrete).
. Averaged Facts with the columns: Mnemonic, Format­type, Flags, Start
Time, Stop Time, Start Millsec, Averaged, Maximum, Minimum EU Val­
ues, and Number of Samples in period.
and with the dimension tables:
. Mnemonics ­ enumerated all Mnemonics, and other meta­data
. Time ­ enumerated as years, months, days, hours, minutes, seconds
. Milliseconds ­ enumerated as 0 ­ 999
. Format ­ enumerated as the defined HST telemetry format codes

370 Pollizzi and Lezon
. Discretes ­ enumerated as the list of all possible discrete text strings (i.e.,
`on', `o# ', `set clear', `set on', `state level 1', `state level 2', . . . )
4. Benchmarking
The apparent simplicity of the above tables belies the subtlety and the itera­
tions necessary to converge to these tables. Key to this refinement was the use of
benchmarking against a reasonable set data. The importance of benchmarking
against a su#ciently sized dataset cannot be understated. With the traversal
power of data warehouse engines, even the most poorly defined warehouse struc­
ture will be quickly searched when the datasets are of typical RDBMS testing
sizes. For the nuances of a warehouse design to come out, the dataset must
reach a reasonable fraction of the total expected size of the holding. In our case,
we pushed our test warehouse up to 100 gigabytes in testing for a planned size
of 1 to 2 terabytes.
In doing the benchmarks, we constructed a family of queries, each meant
to push some aspect typical of the queries we expected the warehouse to handle.
The queries were specifically meant to validate the e#ciency of the indexing as
it related to the size of the warehouse. In terms of the telemetry data, we ran
the query suite against the warehouse with 12 through 36 weeks of data.
It was only through this process that we were able to understand the im­
plications of our design choices, and then refine the warehouse scheme to that
shown above.
5. Lessons Learned
This prototype e#ort demonstrated both the capabilities and limitations of ware­
housing technology. On the plus side, warehousing technology shows a lot of
promise. Once the design was settled, we were impressed with the performance
and shear data handling capability of the warehouse product. It is clear that this
technology can have significant benefit for those working with reams of discrete
information.
As a weakness, this technology is still quite young. The products are only
beginning to stabilize and one must be prepared for a number of false starts.
For the scientific/engineering user, it is important to realize that warehouse
technology is being driven by the commercial sector. There is little experience on
the part of the vendors in scientific data issues, and in many cases the warehouse
product might have a rich set of functions and primitives for commercial or
financial use ­ but be missing rudimentary scientific ones. In particular, be
aware of the use of time, high precision real numbers, scientific notation and
functions. Most importantly, it must be remembered that a data warehouse is
not designed as one would design a relational database. The warehouse designer
has new indexing tools that tend to drive the design more, and the tight linkage
between the logical and physical designs must also be reckoned with.
Finally, the critical lesson is the absolute need for benchmarking a sizable
dataset. It was only through the actual trial­and­error of manipulating the
design, and pushing the technology against a fairly sized dataset, that the real
power and limitations of the warehouse are exposed.